Recent Submissions

  • Gate-opening criterion for generating dam-break flow in non-rectangular wet bed channels

    Yang, S.; Wang, B.; Guo, Yakun; Zhang, J.; Chen, Y. (2020-11)
    A sudden dam failure is usually simulated by the rapid removal of a gate in laboratory tests and numerical simulations. The gate-opening time is often determined according to the Lauber and Hager instantaneous collapse criterion (referred to as Lauber-Hager criterion) established for a rectangular open channel with a dry bed. However, this criterion is not suitable for non-rectangular channels or initial wet-bed conditions. In this study, the effect of the gate-opening time on the wave evolution is investigated by using the large eddy simulation (LES) model. The instantaneous dam break, namely the dam break without a gate, is simulated for comparison. A gate-opening criterion for generating dam-break flow in non-rectangular wet bed channel is proposed in this study, which can be used as an extension of the Lauber-Hager criterion and provides a more comprehensive and reasonable estimate of the gate opening time.
  • Element failure probability of soil slope under consideration of random groundwater level

    Li, Z.; Chen, Y.; Guo, Yakun; Zhang, X.; Du, S. (2021)
    The instability of soil slopes is directly related to both the shear parameters of the soil material and the groundwater, which usually causes some uncertainty. In this study, a novel method, the element failure probability method (EFP), is proposed to analyse the failure of soil slopes. Based on the upper bound theory, finite element discretization, and the stochastic programming theory, an upper bound stochastic programming model is established by simultaneously considering the randomness of shear parameters and groundwater level to analyse the reliability of slopes. The model is then solved by using the Monte-Carlo method based on the random shear parameters and groundwater levels. Finally, a formula is derived for the element failure probability (EFP) based on the safety factors and velocity fields of the upper bound method. The probability of a slope failure can be calculated by using the safety factor, and the distribution of failure regions in space can be determined by using the location information of the element. The proposed method is validated by using a classic example. This study has theoretical value for further research attempting to advance the application of plastic limit analysis to analyse slope reliability.
  • The fluid mechanics of tensioned web roll coating

    Benkreira, Hadj; Shibata, Yusuke; Ito, K. (2021)
    Tensioned web-roll coating is widely used but has surprisingly received little research attention. Here, a new semi-empirical model that predicts film transfer from applicator roller to web is developed and tested against data collected from a pilot coating line. The film transfer is found to vary linearly with web to applicator speed ratio S. Flow stability investigations revealed three types of defects: rivulets, air entrainment due to dynamic wetting failure and cascade, occurring at different values of S and capillary number Ca. Rivulets occurred at Ca< 0.4 and S> 0.71-0.81, air entrainment at Ca>0.4 and S>0.71-0.83 and cascades at S>1.1 for Ca up to 6. Web speeds at which dynamic wetting failure occurred were, for the same Ca, comparatively higher than those that occur in dip coating. The data show that such hydrodynamic assistance is due to the coating bead being confined, more so with increasing web wrap angle β.
  • Turbulence structure and momentum exchange in compound channel flows with shore ice covered on the floodplains

    Wang, F.; Huai, W.; Guo, Yakun; Liu, M. (2021-04)
    Ice cover formed on a river surface is a common natural phenomenon during winter season in cold high latitude northern regions. For the ice-covered river with compound cross-section, the interaction of the turbulence caused by the ice cover and the channel bed bottom affects the transverse mass and momentum exchange between the main channel and floodplains. In this study, laboratory experiments are performed to investigate the turbulent flow of a compound channel with shore ice covered on the floodplains. Results show that the shore ice resistance restricts the development of the water flow and creates a relatively strong shear layer near the edge of the ice-covered floodplain. The mean streamwise velocity in the main channel and on the ice-covered floodplains shows an opposite variation pattern along with the longitudinal distance and finally reaches the longitudinal uniformity. The mixing layer bounded by the velocity inflection point consists of two layers that evolve downstream to their respective fully developed states. The velocity inflection point and strong transverse shear near the interface in the fully developed profile generate the Kelvin-Helmholtz instability and horizontal coherent vortices. These coherent vortices induce quasi-periodic velocity oscillations, while the dominant frequency of the vortical energy is determined through the power spectral analysis. Subsequently, quadrant analysis is used in ascertaining the mechanism for the lateral momentum exchange, which exhibits the governing contributions of sweeps and ejections within the vortex center. Finally, an eddy viscosity model is presented to investigate the transverse momentum exchange. The presented model is well validated through comparison with measurements, whereas the constants α and β appeared in the model need to be further investigated.
  • An improved distortion compensation approach for additive manufacturing using optically scanned data

    Afazov, S.; Semerdzhieva, E.; Scrimieri, Daniele; Serjouei, A.; Kairoshev, B.; Derguti, F. (2021-02)
    This paper presents an improved mathematical model for calculation of distortion vectors of two aligned surface meshes. The model shows better accuracy when benchmarked to an existing model with exceptional mathematical conditions, such as sharp corners and small radii. The model was implemented into a developed distortion compensation digital tool and applied to an industrial component. The component was made of Inconel 718 and produced by laser powder bed fusion 3D printing technology. The digital tool was utilised to compensate the original design geometry by pre-distortion of its original geometry using the developed mathematical model. The distortion of an industrial component was reduced from approximately ±400 µm to ±100 µm for a challenging thin structure subjected to buckling during the build process.
  • Computational intelligence for safety assurance of cooperative systems of systems

    Kabir, Sohag; Papadopoulos, Y. (2020-12)
    Cooperative Systems of Systems (CSoS) including Autonomous systems (AS), such as autonomous cars and related smart traffic infrastructures form a new technological frontier for their enormous economic and societal potentials in various domains. CSoS are often safety-critical systems, therefore, they are expected to have a high level of dependability. Due to the open and adaptive nature of the CSoS, the conventional methods used to provide safety assurance for traditional systems cannot be applied directly to these systems. Potential configurations and scenarios during the evolving operation are infinite and cannot be exhaustively analysed to provide guarantees a priori. This paper presents a novel framework for dynamic safety assurance of CSoS, which integrates design time models and runtime techniques to provide continuous assurance for a CSoS and its systems during operation.
  • Numerical behaviour of buried flexible pipes in geogrid-reinforced soil under cyclic loading

    Elshesheny, Ahmed; Mohamed, Mostafa H.A.; Nagy, N.M.; Sheehan, Therese (2020-06)
    Three-dimensional finite element models were executed and validated to investigate the performance of buried flexible high-density Polyethylene (HDPE) pipes, in unreinforced and multi-geogrid-reinforced sand beds, while varying pipe burial depth, number of geogrid-layers, and magnitude of applied cyclic loading. Geogrid-layers were simulated considering their geometrical thickness and apertures, where an elasto-plastic constitutive model represented its behaviour. Soil-geogrid load transfer mechanisms due to interlocked soil in-between the apertures of the geogrid-layer were modelled. In unreinforced and reinforced cases, pipe burial depth increase contributed to decreasing deformations of the footing and pipe, and the crown pressure until reaching an optimum value of pipe burial depth. On the contrary, the geogrid-layers strain increased with increasing pipe burial depth. A flexible slab was formed due to the inclusion of two-geogrid-layers, leading to an increase in the strain in the lower geogrid-layer, despite its lower deformation. Inclusion of more than two geogrid-layers formed a heavily reinforced system of higher stiffness, and consequently, strain distribution in the geogrid-layers varied, where the upper layer experienced the maximum strain. In heavily reinforced systems, increasing the amplitude of cyclic loading resulted in a strain redistribution process in the reinforced zone, where the second layer experienced the maximum strain.
  • Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living

    Oguntala, George A.; Hu, Yim Fun; Alabdullah, Ali A.S.; Abd-Alhameed, Raed A.; Ali, Muhammad; Luong, D.K. (2021)
    IEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.
  • A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

    Park, S.H.; Leka, K.D.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falconer, D.; et al. (2020-02-19)
    A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.
  • Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

    Williams, S.; Relton, S.D.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Graham, C.D.; Wong, D.C. (2020-11)
    Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.
  • The Automated Prediction of Solar Flares from SDO Images Using Deep Learning

    Abed, Ali K.; Qahwaji, Rami S.R.; Abed, A. (2021-04-15)
    In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).
  • Eccentric compression behaviour of concrete columns reinforced with steel-FRP composite bars

    Ge, W.; Chen, K.; Guan, Z.; Ashour, Ashraf F.; Lu, W.; Cao, D. (Elsevier, 2021)
    Eccentric compression behaviour of reinforced concrete (RC) columns reinforced by steel-FRP composite bars (SFCBs) was investigated through experimental work and theoretical analyses. The tension and compression test results show that SFCBs demonstrate a stable post-yield stiffness. The mechanical properties of the composite reinforcement have a significant influence on eccentric compression behaviour of the reinforced concrete columns, in terms of failure mode, crack width, deformation and bearing capacity. Formulae were also developed to discriminate failure mode and to determine moment magnification factor, bearing capacity and crack width of the columns studied, with the theoretical predictions being in a good agreement with the experimental results. In addition, parametric studies were conducted to evaluate the effects of mechanical properties of reinforcement, reinforcement ratio, eccentricity, slenderness ratio, types of reinforcement and concrete on the eccentric compression behaviour of RC columns. The results show that the compressive performance is significantly improved by using the high performance concrete, i.e. reactive powder concrete (RPC) and engineered cementious composites (ECC).
  • Integrated condition-based maintenance modelling and optimisation for offshore wind turbines

    Dao, Cuong D.; Kazemtabrizi, B.; Crabtree, C.J.; Tavner, P.J. (2021)
    Wind Energy published by John Wiley & Sons Ltd. Maintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition-based maintenance has attracted much attention in the offshore wind industry in recent years. This paper models various maintenance activities and their impacts on the degradation and performance of offshore wind turbine components. An integrated maintenance strategy of corrective maintenance, imperfect time-based preventive maintenance and condition-based maintenance is proposed and compared with other traditional maintenance strategies. A maintenance simulation programme is developed to simulate the degradation and maintenance of offshore wind turbines and estimate their performance. A case study on a 10-MW offshore wind turbine (OWT) is presented to analyse the performance of different maintenance strategies. The simulation results reveal that the proposed strategy not only reduces the total maintenance cost but also improves the energy generation by reducing the total downtime and expected energy not supplied. Furthermore, the proposed maintenance strategy is optimised to find the best degradation threshold and balance the trade-off between the use of condition-based maintenance and other maintenance activities.
  • Protection of buried rigid pipes using geogrid-reinforced soil systems subjected to cyclic loading

    Elshesheny, Ahmed; Mohamed, Mostafa H.A.; Sheehan, Therese (2020-08)
    The performance of buried rigid pipes underneath geogrid-reinforced soil while applying incrementally increased cyclic loading was assessed using a fully instrumented laboratory rig. The influence of varying two parameters of practical importance was investigated; the pipe burial depth and the number of geogrid-layers. Measurements were taken for pipe deformation, footing settlement, strain in pipe and reinforcing layers, and pressure/soil stress on the pipe crown during various stages of cyclic loading. The research outcomes demonstrated a rapid increase in the rate of deformation of the pipe and the footing, and the rate of generated strain in the pipe and the geogrid-layers during the first 300 cycles. While applying further cycles, those rates were significantly decreased. Increasing the pipe burial depth and number of geogrid-layers resulted in reductions in the footing and the pipe deformations, the pressure on pipe crown, and the pipe strains. Redistribution of stresses, due to the inclusion of reinforcing layers, formed a confined zone surrounding the pipe providing it with additional lateral support. The pipe invert experienced a rebound, which was found to be dependent on pressure around the pipe and the degree of densification of the bedding layer. Data for strains measured in the geogrid-layers showed that despite the applied loading value and the pipe burial depth, the tensile strain in the lower geogrid-layer was usually higher than that measured in the upper layer.
  • Process simulation and evaluation of ethane recovery process using Aspen-HYSYS

    Rezakazemi, M.; Rahmanian, Nejat; Jamil, Hassan; Shirazian, S. (2018-01)
    In this work, the process of ethane recovery plant was simulated for the purpose of Front End Engineering Design. The main objective is to carry out a series of simulation using Aspen HYSYS to compare recovery of ethane from Joule Thomson (JT) Valve, Turbo-Expander and Twister Technology. Twister technology offers high efficiency, more ethane recovery and lower temperature than JT valve and turbo-expander process. It lies somewhere between isenthalpic and isentropic process due to its mechanical configuration. Three processes were compared in terms of recovery of ethane. To conduct the simulations, a real gas plant composition and design data were utilized to perform the study for comparison among chosen technologies which are available for ethane recovery. The same parameters were used for the comparisons. Effect of operating conditions including pressure, temperature, and flow rate as well as carbon dioxide on the recovery of ethane was examined.
  • Investigation about profitability improvement for synthesis of benzyl acetate in different types of batch distillation columns

    Aqar, D.Y.; Rahmanian, Nejat; Mujtaba, Iqbal M. (2018-08-01)
    In this work, for the first time, the synthesis of benzyl acetate via the esterification of acetic acid and benzyl alcohol is investigated in the reactive distillation system using a middle vessel (MVD), inverted (IBD), and conventional batch reactive distillation columns. The measurement of the performance of these column schemes is determined in terms of profitability through minimization of the batch time for a defined separation task. The control variables (reboil ratio for MVD, IBD columns) and (reflux ratio in case of CBD column) are considered as piecewise constants over batch time. The optimization results obviously indicate that the CBD system is a more attractive process in terms of batch time reduction, and maximum achievable yearly profit as compared to the MVD, and IBD operations.
  • An experimental investigation on seeded granulation of detergent powders

    Rahmanian, Nejat; Halmi, M.H.; Choy, D.; Patel, R.; Yusup, S.; Mujtaba, Iqbal M. (2016-08-20)
    Granulation is commonly used as an enlargement process of particles produce granules with desirable characteristics and functionality. Granulation process transforms fine powders into free-flowing, dust-free granules with the presence of liquid binder at certain operating conditions. The main focus of this research is on seeded granulation of detergent powders, a new phenomenon of granulation in which a layer of fine powders surround the coarse particle. This is already proven for calcium carbonate (Rahmanian et al., 2011). Here, detergent granules were produced in a 5 L high shear Cyclomix granulator using different fine/coarse powder ratio (1/3, 1, 3) and different binder ratio of 10 %, 20 % and 30 %. The granules were then characterized for their particle size distribution, strength and structure. It was found that a high percentage (70 wt. %) of granules in the desired size range between 125 - 1,000 µm were produced using the powder ratio of 1/3 and a binder content of 10 %. Low mean crushing strength (3.0 N) with a narrow distribution was obtained using this condition. Structure characterization of the detergent granules produced in the granulator shows that consistent seeded granule structures are produced under the optimum process and formulation conditions of 1/3 powder ratio with 10 % binder.
  • Effect of various packing structure on gas absorption for enhanced CO2 capture

    Rahmanian, Nejat; Rehan, M.; Sumani, A.; Nizami, A.S. (2018-08-01)
    The increasing concentration of carbon dioxide (CO2) in the atmosphere is a primary global environmental concern due to its detrimental impacts on climate change. A significant reduction in CO2 generation together with its capture and storage is an imperative need of the time. CO2 can be captured from power plants and other industries through various methods such as absorption, adsorption, membranes, physical and biological separation techniques. The most widely used systems are solvent based CO2 absorption method. The aim of this study was to analyze the effect of various random and structured packing materials in absorption column on CO2 removing efficiency. Aspen plus was used to develop the CO2 capture model for different packing materials with Monoethanolamine (MEA) solvent in order to optimize the system. It was found that the lowest re-boiler duty of 3,444 kJ/KgCO2 yield the highest rich CO2 loading of 0.475 (mole CO2/mole MEA) by using the BX type of structured packing having the highest surface area. The surface area of the different packing materials were inversely proportional to the temperature profiles along the column. Furthermore, the packing materials with higher surface areas yielded higher CO2 loading profiles and vice versa. The findings of this study and recommendation would help further research on optimization of solvent-based CO2 capturing technologies.
  • Energy Savings in CO2 Capture System through Intercooling Mechanism

    Rehan, M.; Rahmanian, Nejat; Hyatt, Xaviar; Peletiri, Suoton P.; Nizami, A.-S. (2017-01)
    It has been globally recognized as necessary to reduce greenhouse gas (GHG) emissions for mitigating the adverse effects of global warming on earth. Carbon dioxide (CO2) capture and storage (CCS) technologies can play a critical role to achieve these reductions. Current CCS technologies use several different approaches including adsorption, membrane separation, physical and chemical absorption to separate CO2from flue gases. This study aims to evaluate the performance and energy savings of CO2capture system based on chemical absorption by installing an intercooler in the system. Monoethanolamine (MEA) was used as the absorption solvent and Aspen HYSYS (ver. 9) was used to simulate the CO2capturing model. The positioning of the intercooler was studied in 10 different cases and compared with the base case 0 without intercooling. It was found that the installation of the intercooler improved the overall efficiency of CO2recovery in the designed system for all 1-10 cases. Intercooler case 9 was found to be the best case in providing the highest recovery of CO2(92.68%), together with MEA solvent savings of 2.51%. Furthermore, energy savings of 16 GJ/h was estimated from the absorber column alone, that would increase many folds for the entire CO2capture plant. The intercooling system, thus showed improved CO2recovery performance and potential of significant savings in MEA solvent loading and energy requirements, essential for the development of economical and optimized CO2capturing technology.
  • Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems

    Truc, George; Rahmanian, Nejat; Pishnamazi, M. (2021-02-26)
    Carbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO2-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO2 with N2 and CH4. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO2 with N2, CH4, Ar, and O2) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture.

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