Empirical Correlations Research Articles

Statistical Reappraisal of the Wax and Mercury Methods for Shrinkage Limit Determinations of Fine-Grained Soils

Because of the hazards associated with handling mercury, most standards organizations have withdrawn the conventional mercury (displacement) method (MM) for shrinkage limit (SL) determination of fine-grained soils. Despite attempts to substantiate the wax (coating) method (WM), which is presently the only standardized MM-testing alternative, the geotechnical community remains somewhat hesitant of its adoption in routine practice. To encourage more widespread use of WM-testing, this study re-examines the level of agreement between the MM- and WM-deduced SL parameters (i.e., SLMM and SLWM, respectively). This was achieved by performing comprehensive statistical analyses on the largest and most diverse database of its kind, to date, entailing SLMM:SLWM measurements for 168 different fine-grained soils having wide ranges of plasticity characteristics (i.e., liquid limit = 31.6–362.0%, plasticity index = 8.2–318.0% and SLMM = 7.1–42.0%). Furthermore, an attempt was made to evaluate the SLWM (in lieu of the SLMM) parameter for performing preliminary soil expansivity assessments using existing SLMM-based classification approaches. It was demonstrated that the MM and WM methods do not produce identical SL values for a given fine-grained soil under similar testing conditions, with their discrepancy being systematic and hence likely arising from the differences between the materials (mercury versus wax) and methodologies involved in performing these tests. New SLWM → SLMM conversion relationships were established, allowing SLMM to be deduced as a function of SLWM with high accuracy. Hence, when inputting SLWM in SLMM-based empirical correlations to predict other geoengineering design parameters, the newly proposed conversion relationships can be employed to minimize systematic prediction errors. It was also demonstrated that plasticity-based correlations, at best, can only provide a rough approximation of SLMM. Hence, when the SL is desired, WM-testing or any other alternative method that directly and reliably measures the soil shrinkage factors should be retained. Finally, the same soil-expansivity rankings, as obtained for existing classification systems employing SLMM results, are achieved using SLWM measurements (i.e., without the need of applying SLWM → SLMM conversion equations).

PARAMETRIC EVALUATION OF HEAT RECOVERY IN A RECUPERATIVE HEAT EXCHANGER

As urban centers grow and environmental regulations become more stringent, the complexity of integral systems within vehicles, aircraft, and other urban essentials escalates. A pivotal response to this challenge involves achieving enhanced energy efficiency and environmental appeal while maintaining cost-effectiveness. In this context, mathematical modeling, coupled with simulation and optimization techniques, emerges as a pivotal tool. This approach yields favorable outcomes with modest initial investments, contrasting with the resource-intensive nature of purely experimental design. Amongst the fundamental components, heat exchangers find widespread use, facilitating thermal exchange between fluids across diverse applications. Consequently, meticulous design and parametric optimization of these devices to attain peak performance and optimal energy efficiency are imperative, aligning with evolving environmental and energy trends. The simulation of such systems operates within an expansive range of operational and geometric parameters. These encompass mass flow rates, line pressures, pipe diameters, and pipe placements. However, excessive parameter combinations can render optimization computationally infeasible, necessitating judicious simplifications. Striking a balance between precision and computational efficiency, reduced-order models present a valuable intermediary solution. These models, situated between low- and high-order methods, offer robust mathematical representations without significant precision compromises. Thus, reduced-order models, which constitute an intermediate approach when compared to low- and high-order methods, can be used as a mathematical modeling tool without a significant loss of precision in the results. The present work presents an optimization and parametric analysis of a recuperative heat exchanger using a reduced-order approach employing the volume element model (VEM) as a discretization method, which is capable of providing accurate results at low costs. computational. The Laws of Conservation of Mass and Energy are applied to volume elements in combination with empirical correlations in order to quantify the quantities of interest, such as the convection heat transfer coefficient and temperature distribution. A parametric analysis was performed in order to observe the behavior of entropy generation in order to find its minimum points. The mass flow of water varied from 0.001 kg/s to 0.0085 kg/s with the mass flow of hot gases and the mass flow rate of gas was held constant in three stages, namely: 0.14 kg/s; 0.2 kg/s; and 0.3 kg/s. The local minimum was obtained for each of the three gas mass flow rate considerations, 8.53 W/K, 8.78 W/K, and 9.20 W/K respectively.

Experimental study on convective heat transfer characteristics of fluoride salt outside the tube bundle in hairpin heat exchanger

Convective heat transfer performances of fluoride-salt flowing outside U-tubes with 4871 < Re < 12298 and 11 < Pr < 17 in hairpin heat exchanger are experimentally investigated. The effects of temperature on the heat transfer coefficient of fluoride-salt are studied with the temperature 540–598.7 °C. Results show that the measured Nusselt number is in line with the empirical heat transfer correlation, but it is slightly larger than that predicted by conventional correlations and the deviations increases as Prandtl numbers increase. It is presumably that the special geometric construction with densely packed bundles and U-bend in hairpin heat exchanger disrupt the boundary layer resulting in the formation of back-flow zone and then strengthen the heat transfer. Therefore, previous correlations are insufficient in describing the fluoride-salt performance. Finally, based on the model of Sieder-Tate correlation, an improved correlation considering a comprehensive heat transfer enhance is proposed, and shows a good agreement with the experiment data with deviation less than 4.8 %.

Numerical analysis and experimental comparison of temperature-compensation method for large length–diameter ratio thermal mass flowmeter

Thermal mass flowmeters (TMFs) are inherently sensitive to temperature fluctuations, which can result in inaccurate measurements. This study focuses on a novel TMF named the large length–diameter ratio thermal mass flowmeter (LLRTMF), which was previously proposed for its insensitivity to the distribution of radial flow velocity and implemented via a variable-speed, variable-temperature experimental setup. The power characteristics of the LLRTMF are investigated over velocity and temperature ranges of 1.00–6.00 m/s and 283.0–319.0 K, respectively. The experimental results indicate that the maximum relative error of the experimental power consumption is 21.96 %. Additionally, a full-size numerical model of the LLRTMF is developed to simulate its power characteristics across wider velocity and temperature ranges of 0.25–16.00 m/s and 283.0–353.0 K, respectively. Experimental data are used to verify the accuracy of the simulation model. Using the numerical model, an empirical correlation equation between power and velocity is derived and polynomially fitted to establish a temperature-compensation equation. The results of the temperature compensation equations and experiment were compared. The maximum relative error of the absolute value was 20.84 %, i.e., less than the maximum error in the results of the experiment due to temperature change, confirming the validity of the formula.

Machine learning-based Nusselt number prediction for falling-film evaporators in absorption refrigeration systems

The evaporator serves as a pivotal component of an absorption refrigeration system (ARS), bolstered by improved wettability and mitigation of the irregular arrangement of the liquid film. The complexities of the evaporation heat transfer characteristics of falling films have led to numerous empirical correlations for falling-film Nusselt number (Nu) estimation, demanding substantial computational resources and experimental expenses and limiting their universal applicability. This study used machine learning to predict Nu of a falling-film evaporator. Artificial neural network (ANN) and random forest regression (RFR) models were used to predict heat transfer characteristics and establish a novel Nu correlation using an experimentally derived dataset. Correlations of falling-film Nu were established for six tube types using empirical data, ANN, and RFR predictions and then compared based on experimental falling-film Nu. The ANN and RFR provided high prediction performance within 10 % error and an R2 of 0.985 and 0.999, respectively. The Nu correlations derived from ANN and RFR demonstrated satisfactory agreement, typically within ±15 % error of experimental results lower than that of the empirical correlations. The optimized ANN and RFR demonstrates their promising ability to predict heat transfer characteristics and establish Nu correlations, thereby reducing the need for immense experimental efforts.

Development of Empirical Correlations Between CPT and Other Soil Parameters Within DMDP Area, Bangladesh

Development of Empirical Correlations Between CPT and Other Soil Parameters Within DMDP Area, Bangladesh

Laminar and kinetic burning characteristics of ethanol/methane/hydrogen fuel: Experimental and numerical analysis

In this essay, the effects of ethanol addition on the fuels of Hydrogen methane mixing fuel were discussed. The laminar burning characteristics of ethanol/hydrogen/methane mixture under different ethanol content, pressure (1 bar, 2 bar), and equivalence ratios (0.7–1.4) were experimentally and numerically simulated. Results show that increasing ethanol content reduces the laminar burning velocity (LBV) and shifts the peak LBV towards higher equivalence ratios, attributed to the fuel mixture's decreased average combustion heat. For a 20 % ethanol content, the LBV peak appears at φ = 1.3, while for 50 % and 80 % ethanol content, it appears at φ = 1.2 locations. An empirical correlation for LBV as a function of ethanol concentration was developed, demonstrating predictions within a 4 % error margin compared to experimental data. High accuracy fittings of activation energy, ethanol concentration, and equivalence ratio were achieved, with correlation coefficients exceeding 0.9. Numerical simulations pinpointed key reactions and the relationship between free radicals and LBV, highlighting a pronounced correlation between OH radicals and LBV under lean conditions (coefficient of 1.0583), and a strong correlation with H radicals under rich conditions (coefficient of 1.01657), this indicates that OH and H are involved in the key chain reaction that promote the burning process.

Experimental investigation of countercurrent flow limitation (CCFL) in a 1/19 scale of VVER-1000 geometry

Countercurrent flow limitation (CCFL) has been experimentally considered in a VVER-1000 hot-leg. The proposed test facility consists of a hot-leg pipe, a reactor vessel simulator (RVs), and a steam generator simulator (SGs). It was designed and constructed based on power-to-volume scaling approach by preserving the Froud number in the hot leg. The scaling ratio was considered 1/19 of the Bushehr NPP (VVER-1000) geometry. The hot leg of the proposed facility provides good optical observation of the gas/liquid interface. The SG simulator has also a side view for optical observation. Experimental results were captured at atmospheric pressure with air/water as fluid. CCFL was obtained by analyzing the pressure differences between RV and SG simulators.This CCFL data was compared with empirical data and correlations available in the literature. With increasing liquid superficial velocity, the gas superficial velocity curve is continuously decreased, which is consistent with Navarro's results. In this study, an experimental correlation is proposed and compared to other correlations. It was very close to the Navarro and Richter correlations.The results of experimental investigations clarified the onset of the CCFL phenomenon in a Bushehr NPP (VVER-1000) hot-leg geometry. Moreover, the achieved patterns and behaviors of the air/water mixture would be used in computational fluid dynamics (CFD) code validations.

On the dynamics and wakes of a freely settling Platonic polyhedron in a quiescent Newtonian fluid

We investigate systematically the free settling of a single Platonic polyhedron in an unbounded domain filled with an otherwise quiescent Newtonian fluid. We consider a particle–fluid density mimicking a rock in water. Five Platonic polyhedrons of increasing sphericity are studied for a range of Galileo numbers $10 \leqslant \mathcal {G}a \leqslant 300$ . We construct a regime map in the parameter space of Galileo number and particle volume fraction ( $\mathcal {G}a, \phi$ ), highlighting how the angularity of the Platonic polyhedron impacts its settling path and the onset of instabilities. We find that the initial angular position solely affects the transient settling process. All the Platonic polyhedrons maintain a stable settling angular position at low $\mathcal {G}a$ . Higher angularity leads to path unsteadiness at lower $\mathcal {G}a$ . Path instability progresses from steady vertical to unsteady vertical, followed by oblique settling observed for highly spherical particles, but helical settling (HS) for more angular particles. The particle autorotation is found to be the pivotal factor influencing path instability and the regime transition of angular particles. Beginning in the unsteady oblique and helical regimes, particle autorotation becomes more prevalent, escalating further in the chaotic regime as $\mathcal {G}a$ increases. The particle angular velocity vector is shown to be predominantly situated in the horizontal plane. A thorough force balance in the horizontal plane reveals that the Magnus force is the primary driving force of the HS regime. Additionally, we establish two new empirical correlations to predict the particle settling velocity and the disturbed wake length that solely require the physical properties of the system ( $\mathcal {G}a$ and $\phi$ ). Our numerical results suggest that an increase of the density ratio from $2$ to $3$ exerts only a marginal impact on the path instability of the most angular particle, the settling tetrahedron.

A correlated multi-observable assessment for vanadium redox flow battery state of charge estimation — Empirical correlations and temperature dependencies

The efficient operation of vanadium redox flow batteries requires the half cell specific monitoring of the state of charge (SOC). Monitoring of the SOC is affected by temperature fluctuations, which need to be distinguished from the SOC signal. Several SOC monitoring approaches have been proposed in the literature and either been evaluated individually or compared to one other approach. The aim of this study is to contribute to SOC sensor development for VRFB by providing a first correlated assessment of several established and new SOC monitoring methods. We perform multi observable measurements of: half cell electrolyte potentials, electrolyte densities, electrolyte volumes, electrolyte viscosity related pressure drops, pH related potentials and UV/Vis absorbances. We determine SOC correlations of these observables in full charge/discharge cycles at constant temperature and derive temperature corrections in additional measurements over a range of 12°C to 32°C at 25%, 50% and 75% SOC. We compare the SOC and temperature sensitivity as well as the accuracy of these monitoring methods at constant and varied temperature. We find that the established half cell electrolyte potentials and UV/Vis absorbances can estimate the SOC at constant temperature with measurement errors of 1.7% and 4%, respectively and with slightly higher errors at varying temperatures. The viscosity related pressure drop and electrolyte density are both suitable for SOC estimation if temperature corrections are included. The pH related potentials for the positive half cell and both half cell electrolyte volumes could be used for a rough SOC estimate, but need accurate temperature corrections and further long term stability evaluation.

Generalizability of empirical correlations for predicting higher heating values of biomass

ABSTRACT Designing efficient biomass energy systems requires a thorough understanding of the physicochemical, thermodynamic, and physical properties of biomass. One crucial parameter in assessing biomass energy potential is the higher heating value (HHV), which quantifies its energy content. Conventionally, HHV is determined through bomb calorimetry, but this method is limited by factors such as time, accessibility, and cost. To overcome these limitations, researchers have proposed a diverse range of empirical correlations and machine-learning approaches to predict the HHV of biomass based on proximate and ultimate analysis results. The novelty of this research is to explore the universal applicability of the developed empirical correlations for predicting the Higher Heating Value (HHV) of biomass. To identify the best empirical correlations, nearly 400 different biomass feedstocks were comprehensively tested with 45 different empirical correlations developed to use ultimate analysis (21 different empirical correlations), proximate analysis (16 different empirical correlations) and combined ultimate-proximate analysis (8 different empirical correlations) data of these biomass feedstocks. A quantitative and statistical analysis was conducted to assess the performance of these empirical correlations and their applicability to diverse biomass types. The results demonstrated that the empirical correlations utilizing ultimate analysis data provided more accurate predictions of HHV compared to those based on proximate analysis or combined data. Two specific empirical correlations including coefficients for each element (C, H, N) and their interactions (C*H) demonstrate the best HHV prediction with the lowest MAE (~0.49), RMSE (~0.64), and MAPE (~2.70%). Furthermore, some other empirical correlations with carbon content being the major determinant also provide good HHV prediction from a statistical point of view; MAE (~0.5–0.8), RMSE (~0.6–0.9), and MAPE (~2.8–3.8%).

Experimental and Numerical Investigations on Thermal-Hydraulic Performance of Three-Dimensional Overall Jagged Internal Finned Tubes.

To satisfy the demand for efficient heat transfer, a novel three-dimensional overall jagged internal finned tube (3D-OJIFT) was fabricated, using the rolling-ploughing/extruding method. The thermal performance of the 3D-OJIFT were studied and compared in experiments and three-dimensional numerical simulations. The RNG k-ε turbulence model is well verified with the experimental results. By analyzing the distributions of velocity, temperature, and turbulence kinetic energy, it was found that the 3D-OJIFT destroyed the development of the velocity and thermal boundary layers, increased the turbulence disturbance, and reduced the temperature gradient, thus improving the heat transfer. The influences of the jagged height and jagged spiral angle of the 3D-OJIFT are discussed. The Nu and f increased as the jagged height of the 3D-OJIFT increased. The Nusselt number of the 3D-OJIFT was 1.67-2.04 times the value for the smooth tube. In addition, the comprehensive heat transfer performance of the 3D-OJIFT improved after increasing the jagged spiral angle. Compared with conventional internal helical-finned tubes and other reinforcement structures reported in the literature, the 3D-OJIFT demonstrated better comprehensive heat transfer performance. Finally, empirical correlations of the 3D-OJIFT were obtained.

Estimating droplet size and count distributions over a prolonged period of time following a cough in indoor environments

An empirical correlation and a set of machine learning (ML) models were developed to estimate droplet size and count distributions over an extended duration after a cough at different relative humidities (RHs), air temperatures and locations within an indoor environment. Experiments covered RHs of 20%–80% and air temperatures of 21 °C–26 °C. Droplet count distributions for 4 size bins (0.3–0.5, 0.5–1, 1–3 and 3–5 μm) were recorded for 70 min within the distance of 2 m from the cough source. Different ML models, including decision tree, random forest and artificial neural network, were trained for each size bin to predict the associated count distribution. Amongst these models, random forest showed a slight superiority in performance. The coefficient of determination for the random forest models ranged from 0.912 to 0.989, indicating robust correlations between the features and the response variables. An empirical correlation was established linking the count distribution of 0.3–0.5 μm droplets to time, RH and distance along the cough direction. Both ML models and the correlation accurately predicted the trends and the distributions, providing valuable data for validating computational simulations and informing indoor environment control systems to reduce the risk of virus transmission.

Ferromagnetic ordering in Fe: TiO2 solid solutions: A visual electron density mapping by maximum entropy method using powder X-ray data

Magnetic semiconducting FeTiO2 solid solutions with Fe compositions of 6 %, 9 %, and 12 % were analyzed using multi-phase Rietveld refinement analysis for average structure and maximum entropy for electronic structure. VSM analysis showed that the 9 % and 12 % Fe doped TiO2 compositions had a maximum magnetization of 0.3128 emu/g and coercivity of 574 Oe and 568 Oe, respectively. This confirms ferromagnetic tuning with Fe doping at or above 9 %. The MEM electron density at the bond critical points (BCP) of the apical and equatorial (Ti/Fe)–O bonds in 12 % Fe-doped TiO2 is 1.088 e/Å3 and 0.8652 e/Å3, indicating polar covalent bond. This work primarily examines a new empirical correlation between MEM electron density and magnetic parameters. Fe: TiO2 solid solutions have high magnetization and coercivity due to strong and identical (Ti/Fe) – O apical and equatorial polar covalent bonding, with low interstitial charge buildup at non-regular tetrahedral rutile lattice sites.

Empirical Correlation between Electrical Conductivity and Nitrogen Content in Biochar as Influenced by Pyrolysis Temperature

Much progress has been made in understanding the conditions of biochar production related to biochar properties and carbon (C). Still, very little knowledge has been gained regarding the effects on nitrogen (N), one of the most critical nutrients affected by pyrolysis temperature (PT). Analysis of N in biochar is costly, and alternative methods should be developed to estimate the N content in biochar quickly under different pyrolysis conditions. We hypothesized that there was a correlation between biochar N content and its electrical conductivity (EC). We aimed to evaluate total N and the effect of PT through the correlation with EC, a parameter that can be easily measured. Biochar products derived from coffee husk (CH) and chicken manure (CM) produced at increasing PT (300 to 750 °C) were used for the study and measured for total N and EC. The increase in PT caused significant N loss, consequently reducing total N content in biochars, with the highest loss (82%) and lowest total N content (1.2 g kg−1) found in CM biochar pyrolyzed at 750 °C. The lowest N loss (21% for CH biochar and 36% for CM biochar) was observed at a PT of 300 °C. A negative correlation between EC and total N and a positive correlation with N loss were found in both biochar products across the wide range of PT investigated. To preserve the N content in biochars, the PT should not exceed 400 °C. Our results indicate that EC is a fast and accurate biochar proxy attribute capable of predicting the N content and its loss in coffee husk and chicken manure-derived biochars as the pyrolysis temperature increased from 300 °C to 750 °C and could be used as an alternative to predict the N in biochar easily. A more extensive set of biochar samples and pyrolysis conditions should be tested to validate this approach.

Machine-learning algorithms for predicting condensation heat transfer coefficients in the presence of non-condensable gases

Condensation on surfaces in the presence of non-condensable gas (NCG) is a ubiquitous and critical phenomenon in many industrial fields. However, current empirical correlations for predicting condensation heat transfer coefficients in the presence of NCG may exhibit significant deviations from reality. Machine learning algorithms are now being dedicated to this field, but the models developed can only predict the condensation heat transfer coefficients of steam or vapor of a non-aqueous working fluid with similar physical properties to water in the presence of NCG. In the present study, a comprehensive theoretical analysis was conducted to investigate the total condensation heat transfer coefficients with NCG, and 16 dimensionless numbers were identified as input variables for machine learning models. Based on a filtered database consisting of 4377 data points extracted from 37 papers, the Spearman correlation coefficients were calculated to evaluate the relationship between the total heat transfer coefficients and the input variables, indicating the magnitude of the impact of the 16 dimensionless variables. Four machine learning models, namely Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), Random Forest Regression (RFR), and Multilayer Perceptron (MLP), were developed to predict the total heat transfer coefficients for water and non-aqueous working fluids. The mean absolute percentage errors for the four models were 1.38%, 1.63%, 3.00%, and 4.42%, respectively, with the GBR model exhibiting the highest degree of accuracy. The determination of the application scope of these models was conducted by analyzing the value ranges for each dimensionless parameter and its corresponding frequency distribution.

A prediction model for railroad ballast resilient modulus considering cyclic loading duration ratio

Railroad ballast aggregate significantly contributes to load distribution, drainage, and track resilience. Over time, the resilience degrades due to factors like ballast breakage, permanent deformation, and mud pumping, necessitating regular, resource-intensive maintenance. Thus, investigating the influential factors of resilient modulus and accurately correlating them can assist in cost-effective maintenance planning. This study used large-scale triaxial tests with varied loading waveforms to investigate their impact on resilient modulus. A new index, the Cyclic Loading Duration Ratio (CLDR), was introduced to quantify loading waveforms with different rest periods. Our experimental results indicate that the cyclic loading duration period controls the resilient modulus of granular material by varying the loading rates, while unloading duration and rest periods do not affect the resilient modulus. With increasing CLDR, resilient modulus initially decreases, but begins to ascend when CLDR falls above 0.20. Building on these findings, an empirical correlation model incorporating the CLDR for the resilient modulus of railroad ballast is proposed. The validation results show this proposed model outperforms existing ones in correlating the resilient modulus with cyclic loading waveforms.

Prediction of entrainment fraction in two-phase gas-liquid co-current annular flow-A machine learning approach

Entrainment fraction is a crucial parameter to determine liquid holdup, pressure drop, and dryout in two-phase annular flow. It determines the fraction of liquid flows as liquid droplets in the gas core. Usually, empirical correlations are used to predict entrainment fraction. However, these correlations forecast entrainment fractions with large Mean Absolute Percentage Error (MAPE), especially when one correlation is used to predict entrainment fractions for different operating conditions. For instance, existing models considered in this study predict entrainment with MAPE of as much as 120.7 %. Besides around 40 % and 24 % of data were outside of ±30 % and ±50 % ranges. In the framework of this study, a Deep Neural Network (DNN model) has been developed to predict entrainment fraction in annular flow that is applicable for different operating conditions with good accuracy. The neural network is trained and tested for around 1213 entrainment fraction data points extracted from 7 different literature studies. It covers pressure ranges 0.1–20 MPa, tube diameter 5–32 mm with working fluid- air/water, steam/water, and Freon-113. The developed DNN model predicts entrainment fraction with the lowest MAPE compared to the existing models on unseen 155 data from 6 authors that are not used to train or test the model. Furthermore, only 17.61 % and 30.81 % of data remain outside of ±30 % and ±20% limits respectively whereas these values are at least 35.66 % and 52.86 % for existing models that are examined for comparison here.

Damping‐dependent correlations between horizontal‐horizontal, horizontal‐vertical, and vertical‐vertical pairs of spectral accelerations

AbstractCorrelation coefficients for 5%‐damped spectral accelerations (SAs) of horizontal ground‐motion components have been extensively studied and applied in probabilistic seismic analysis using vector‐valued intensity measures (IMs). Such correlations are, however, not sufficient for structures with different damping ratios under multidirectional earthquake shakings. This paper presents a comprehensive study on the correlations between horizontal‐horizontal (H‐H), horizontal‐vertical (H‐V), and vertical‐vertical (V‐V) pairs of SAs for different damping ratios based on the NGA‐West2 ground motion database. The correlations of SAs with peak ground acceleration (PGA) and peak ground velocity (PGV) are also investigated. The uncertainty in correlations is measured by integrating the bootstrap method into the logic‐tree framework. Comparative results indicate that the correlation coefficients generally increase for IMs of larger damping ratios. The relative difference between the damping‐dependent and conventional 5%‐damped correlation coefficients can be notable, reaching 100% and 35% in the SA‐SA and SA‐PGV (or PGA) pairs, respectively. Based on the empirical correlation results, an artificial neural network is utilized to develop parametric models of the correlations and the executive files for implementing these models are provided. The ANN‐aided damping‐dependent correlation models developed can be considered as a generalization of the conventional 5%‐damped correlation models, and may serve as useful tool in applications such as ground‐motion selection and vector‐valued probabilistic seismic risk assessment for structure systems.

Prediction of globule size and globule size distribution of emulsion liquid membrane in a pulsed packed column

In this work, the emulsion globule size of an ELM system in a pulsed packed column in different surfactant concentrations was studied for the first time. The emulsion liquid membrane consists of kerosene as a diluent and Span 80 as a surfactant. The effect of surfactant concentration and hydrodynamic parameters, including pulse intensity and flow rates of the dispersed phase and continuous phase, on globule size, was investigated. Based on the results, increasing the concentration of Span 80 up to 3.5 % v/v results in 58–64 % reduction in the Sauter mean diameter (D32) and narrowing the size distribution of the globules. Operating parameters such as pulse intensity and flow rates of continuous and dispersed phases have been investigated so that the influence of flow rates was considerably smaller than the effect of pulse intensity on the globule size distribution. Also, the results showed that with increasing pulse intensity, D32 decreased by 29 %. A new empirical correlation for D32 calculation was also suggested, Featuring 4.91 % average absolute relative error (AARE). Finally, the globule size distribution was predicted using the normal probability function and the dimensionless correlations accurately predicted their parameters and the measured %AARE of α and β were 8.3 and 3.4, respectively.