Average Absolute Relative Error Research Articles

Reliable smart models for estimating frictional pressure drop in two-phase condensation through smooth channels of varying sizes

Reliable and comprehensive predictive tools for the frictional pressure drop (FPD) are of particular importance for systems involving two-phase flow condensation. However, the available models are only applicable to specific operating conditions and channel sizes. Thus, this study aims at developing universal models to estimate the FPD during condensation inside smooth mini/micro and conventional (macro) channels. An extensive databank, comprising 8037 experimental samples and 23 working fluids from 50 reliable sources, was prepared to achieve this target. A comprehensive investigation on the literature models reflected the fact that all of them are associated with high deviations, and their average absolute relative errors (AAREs) exceed 26%. Hence, after identifying the most effective input variables through the Spearman's correlation analysis, three soft-computing paradigms, i.e., multilayer perceptron (MLP), gaussian process regression (GPR) and radial basis function (RBF) were employed to establish intelligent and dimensionless predictive tools for the FPD based on the separated model suggested by Lockhart and Martinelli. Among them, the most accurate results were presented by the GPR approach with AARE and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} values of 4.10%, 99.23% respectively, in the testing step. The truthfulness and applicability of the models were explored through an array of statistical and visual analyses, and the results affirmed the obvious superiority of the newly proposed approaches over the literature correlations. Furthermore, the novel predictive tools excellently described the physical variations of the condensation FPD versus the operating parameters. Ultimately, the order of importance of factors in controlling the condensation FPD was clarified by a sensitivity analysis.

High-temperature mechanical properties of additively manufactured 420 stainless steel

Martensitic stainless steels are indispensable alloys in various high stress and temperature applications such as plastic injection molds and components in steam generators. Subtractive manufacturing methods used to fabricate these parts, however, limits its functionality and performance due to design constraint of cooling channels. This limitation can be resolved by means of additive manufacturing while ensuring that acceptable high-temperature properties can be achieved. In this work, the mechanical behavior of additively manufactured 420 stainless steel (AM420SS) is explored through material constitutive modeling to determine the mathematical model that best describes its flow stress in extreme conditions. This is accomplished by subjecting the samples to hot compression under the strain rates of 0.1–1.0 s−1, and temperatures between 973–1423 K (700 °C–1150 °C) via Gleeble thermomechanical test. The experimental data were used to generate the predictive flow stress curves of constitutive models which includes Johnson-Cook, Zerilli-Armstrong, Zener-Hollomon, and Hensel-Spittel equations. Results showed that Zener-Hollomon and Hensel-Spittel models are the most accurate material constitutive equations with relatively high R values of 0.986 and 0.976, and low average absolute relative error values of 6.96% and 7.69%, respectively. The material constants derived from these models can be applied in finite element analysis simulations to assess the performance of using AM420SS parts at high temperature and strain conditions.

Enhancement of thermal conductivity in PDMS/CFP composites using a dredging-plugging thermal network

In the preparation of thermally conductive polymer composites (TCPC), it is necessary to incorporate a filler network in the polymer matrix. Further enhancement of the thermal conductivity (TC) of TCPCs is challenging due to the exceptional heat absorption of the polymer matrix, which results in heat flow losses in the network. To address this issue, a novel procedure, termed thermal network dredging-plugging, has been proposed. In this study, polydimethylsiloxane (PDMS) composites with a carbon fiber powder (CFP) network and a CFP/glass ball (GB) network enhanced by dredging-plugging were prepared by a coating method. The TC of the PDMS/CFP system increased with an increase in CFP loading from 0 to 12 wt% and a decrease in sample thickness from 0.4 to 0.2 mm. At a 12 wt% CFP loading, the PDMS/CFP composites exhibited a maximum TC of 4.687 W/(mK). With the addition of GBs ranging from 0 to 2 wt%, the dredging-plugging CFP/GB network increased the maximum TC by a factor of 1.26 relative to the dredged CFP network. In conjunction with the thermal resistance method, a mathematical model that considers both the plugging and dilution effects of GBs was constructed. The calculated TCs were validated by using the prepared PDMS/CFP/GB composites, with average absolute relative errors ranging from 0.5 % to 4.19 %. In addition to TC enhancement, the mechanical properties and thermal management of the PDMS composites were evaluated.

Thermal deformation behavior and microstructural evolution of the rapidly-solidified Al–Zn–Mg–Cu alloy in hot isostatic pressing state

Isothermal uniaxial compression experiments were performed in the temperature range of 340–460 °C and the deformation rate range of 0.001–1 s−1 to analyze the hot deformation behavior of the rapidly-solidified Al–Zn–Mg–Cu alloy in hot isostatic pressing state. A strain-compensated constitutive model was established to determine the flow stress in the alloy (based on the true stress-true strain data), and the average activation energy for the hot deformation was calculated as Q = ∼146 kJ/mol. The proposed model exhibited a high predictability with an average absolute relative error of 2.68% and a correlation coefficient of 0.99724. The processing map revealed that the alloy in the hot isostatic pressed state offers better workability than the spray-deposited alloy, and its optimal workable ranges at a strain of 0.78 are 370–390 °C/0.004–0.01 s−1 and 400–460 °C/0.005–0.06 s−1, respectively. The microstructural evolution shows that the main dynamic softening mechanism changes from dynamic recovery to continuous dynamic recrystallization with the increase in temperature and the decrease in strain rate.

Experimental study and machine learning modeling of water removal efficiency from crude oil using demulsifier

This study deals with the investigation of the water removal efficiency (WRE) from crude oil using a commercial demulsifier. The impacts of time, demulsifier concentration, and temperature on WRE were experimentally studied. The results implied the fact that temperature plays a substantial role in the demulsification and has a direct correlation with WRE. In addition, while increasing the concentration up to 40 ppm contributed to reaching a higher WRE, it did not have positive effects on efficiency at higher concentrations (overdose) and just led to more demulsifier consumption. The concentration dependence of WRE was also diminished at high temperatures. At higher levels of temperature and concentration, the time required to reach a high WRE was noticeably reduced. In order to generalize the findings of this study, the measured experimental data were employed to design predictive methods for WRE based on two smart soft-computing paradigms, including Multilayer perceptron (MLP) and Gaussian process regression (GPR). Despite the high accuracy of both models, the MLP model presented the best consistencies with experimental data with average absolute relative error and relative root mean squared error of 0.84%, and 0.01%, respectively during the testing (validation) step. Also, a visual description through the contour diagram confirmed the capability of the recently proposed models to describe the physical variations of WRE under various operating conditions. Ultimately, a sensitivity analysis based on the MLP model was undertaken to shed light on the order of significance of operational factors in controlling WRE. Overall, the findings of the current research, in turn, have a satisfactory contribution to the efficient design of the water removal process from crude oil based on demulsifiers.

Hot Compression Behavior of 18Cr–11Mn–0.06Ni–0.19N High‐Mn Ultralow Nickel Duplex Stainless Steel under Large Deformation

To enhance the rolling production efficiency of high‐manganese low‐nickel duplex stainless steel (DSS) and optimize hot compression parameters, hot compression with a range of 0.01–10 s−1/850–1150 °C and deformations of 70% is used to investigate the hot deformation behavior of 18Cr–11Mn–0.06Ni–0.19 N DSS, which reveals the differences in the recrystallization mechanism of two phases and their thermoplastic properties. The results show that strain partitioning and softening occur first in the ferrite phase, with the austenite phase undergoing complex and incomplete softening. Most ferrite experiences continuous dynamic recrystallization and develops a <001>//ND texture when deformed at 0.1–10 s−1/950 °C, with higher temperatures facilitating the merging of high‐angle grain boundaries. A deformation‐induced phase transformation from δ to γ occurs in ferrite when deformed at 0.01 s−1/850–950 °C. However, austenite softening is dominated by discontinuous dynamic recrystallization, enhanced by twinning‐induced strain‐induced boundary migration when deformed at 0.1 s−1/950 °C; dynamic recovery controlled by dislocations is enhanced when deformed at 0.1 s−1/1150°C and 10 s−1/950 °C. A strain‐compensated constitutive model with an average absolute relative error value of 7.8% is established to better predict the flow stresses. Experimental DSS displays optimal hot workability in the parameter ranges of 0.1 s−1/950–1050 °C.

Modified Fields-Backofen and Zerilli-Armstrong constitutive models to predict the hot deformation behavior in titanium-based alloys

This work presents modifications for two constitutive models for the prediction of the flow behavior of titanium-based alloys during hot deformation. The modified models are the phenomenological-based Fields-Backofen and the physical-based Zerilli-Armstrong. The modifications are derived and suggested by studying the hot deformation of titanium-based alloy Ti55531. The predictability of the modified models along with the original Fields-Backofen and another modified Zerilli-Armstong models is assessed and evaluated using the well-known statistical parameters correlation coefficient (R), Average Absolute Relative Error (AARE), and Root Mean Square Error (RMSE), for the Ti55531 alloy, and validated with other two different titanium-based alloys SP700 and TC4. The results show that the modified Fields-Backofen gives the best performance with R value of 0.996, AARE value of 3.34%, and RMSE value of 5.64 MPa, and the improved version of the modified Zerilli-Armstrong model comes in the second-best place with R value of 0.992, AARE value of 3.52%, and RMSE value of 9.15 MPa for the Ti55531 alloy.

Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications.

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

In order to avoid potential personnel and financial losses, the evaluation of pre-drilling safety is of great importance in oil and gas exploration and development. This paper presents a method of evaluating pre-drilling safety through combining the Analytic Hierarchy Process (AHP) with the Alternating Condition Expectation (ACE) method. An indicator system with a 9-3-1 structure was established, incorporating various unrestricted variables to describe the technical factor. Additionally, nine membership functions and weights were determined in order to build the AHP model by connecting the independent variables in the basic layer to dependent variables in the middle layer. Four transformed functions were also formulated to construct the ACE model by linking the middle variables to the pre-drilling safety value in the final layer. A total of 28 sets of on-site drilling data from three oilfields were collected for the establishment and verification of the AHP-ACE model. Average absolute error (AAE) and average absolute relative error (AARE) of the model to predict the training data are 0.03 and 4.29%, respectively, whereas the AAE and AARE for verification samples are 0.03 and 4.51%, respectively. The sensitivity ranking of the three potential variables is as follows: human factor exhibits the highest degree of sensitivity, followed by natural factor and technical factor, in descending order. The AHP-ACE model for pre-drilling safety assessment faces limitations in universal applicability and scope, particularly in real-time drilling activities. However, its potential for improvement lies in integrating insights from past operations and expanding the dataset to enhance accuracy and broaden safety assessment coverage. This method is not limited by blocks, which is of great significance to ensure drilling safety.

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.

Predicting the strain hardening behavior and constructing processing maps of a lightweight Fe–Mn–Al–C steel through phenomenological modelling

This research deals with predicting the strain hardening behavior and constructing the processing maps of a lightweight steel over a wide range of temperature. To achieve this, the tensile tests were conducted at temperatures of 100 °C–1100 °C under strain rates of 0.001, 0.01, and 0.1s−1 up to fracture. In order to predict the deformation behavior of the lightweight steel, strain compensated Arrhenius-type models were employed. Considering the wide temperature range under investigation, the activation energies and strain rate sensitivity coefficients substantially varied, therefore, three different temperature ranges of 25–200 °C, 300–600 °C, and 700–1000 °C were considered for the prediction of the flow stress level. To evaluate the accuracy of the applied models, the (i) root mean square error (RMSE), (ii) average absolute relative error (AARE), and (iii) correlation coefficient (R), were calculated as well-known statistical parameters. A good agreement was observed between the predicted and actual flow stress. Employing the predicted data, the strain hardening curves were plotted and power dissipation/instability maps were constructed at various strain levels and temperature ranges. The rapid hardening regions, the regions with high efficiency and instability regions were identified which would be valuable for industrialization of the experimented material.

Modelling the Flow Behaviour of Al Alloy Sheets at Elevated Temperatures Using a Modified Zerilli-Armstrong Model and Phenomenological-Based Constitutive Models.

The flow behaviour of AA2060 Al alloy under warm/hot deformation conditions is complicated because of its dependency on strain rates (ε˙), strain (ε), and deformation modes. Thus, it is crucial to reveal and predict the flow behaviours of this alloy at a wide range of temperatures (T) and ε˙ using different constitutive models. Firstly, the isothermal tensile tests were carried out via a Gleeble-3800 thermomechanical simulator at a T range of 100, 200, 300, 400, and 500 °C and ε˙ range of 0.01, 0.1, 1, and 10 s-1 to reveal the warm/hot flow behaviours of AA2060 alloy sheet. Consequently, three phenomenological-based constitutive models (L-MJC, S1-MJC, S2-MJC) and a modified Zerilli-Armstrong (MZA) model representing physically based constitutive models were developed to precisely predict the flow behaviour of AA2060 alloy sheet under a wide range of T and ε˙. The predictability of the developed constitutive models was assessed and compared using various statistical parameters, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). By comparing the results determined from these models and those obtained from experimentations, and confirmed by R, AARE, and RMSE values, it is concluded that the predicted stresses determined from the S2-MJC model align closely with the experimental stresses, demonstrating a remarkable fit compared to the S1-MJC, L-MJC, and MZA models. This is because of the linking impact between softening, the strain rate, and strain hardening in the S2-MJC model. It is widely known that the dislocation process is affected by softening and strain rates. This is attributed to the interactions that occurred between ε and ε˙ from one side and between ε, ε˙, and T from the other side using an extensive set of constants correlating the constitutive components of dynamic recovery and softening mechanisms.

Modifying the gas-oil ratio and water cut of a PVT lookup table using correlations for fluid volatility

PVT lookup tables with fixed fluid composition are widely used by simulation software packages to model complex multiphase flow in petroleum production systems. These tables contain pre-calculated values of various fluid properties, including phase fractions, densities, viscosities, and other properties that are relevant to fluid behaviour. The pre-calculated values are based on a given set of pressures, temperatures, and composition. The tables enable efficient and accurate simulations by reducing computational load. However, they are created for fluids with fixed total composition, which limits their use for modelling fluids with varying gas-oil ratios and water cuts.To address this limitation, this work introduces a novel methodology for adjusting the gas-oil ratio and water cut within a given PVT table without changing the composition of the fluid. The methodology relies on correlations developed for volatility, estimating bubble- and dewpoint pressures when adjusting gas-oil ratio and water cut. The model demonstrates remarkable efficacy, achieving excellent agreement with bubble- and dewpoint pressures, yielding average absolute relative errors of only 0.85% and 0.68% respectively for gas-oil ratio adjustments, and 0.97% for water cut adjustments. It is further demonstrated that the new model accurately predicts phase fractions of typical volatile oils below the critical temperature and black oils when adjusting the gas-oil ratio and water cut. Additionally, the model can provide rough estimates for volatile oils above the critical temperature and for gas condensates. Moreover, model reliability is expected if the gas-oil ratio and water cut adjustments are kept within a range of 20–180% of the original values.The work provides a valuable approach to improve the flexibility of fixed composition PVT lookup tables for simulating multiphase production systems.

Modeling liquid rate through wellhead chokes using machine learning techniques

Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg–Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (Pwh), gas-to-liquid ratio (GLR), and choke size (Dc). A dataset of 565 data points is available for model development. The performance of the two suggested approaches is compared with earlier correlations. Results revealed that the proposed models outperform the existing ones, with the Adaboost-SVR model showing the best performance with an average absolute percent relative error (AAPRE) of 5.15% and a correlation coefficient of 0.9784. Additionally, the results indicated that the developed correlation resulted in better predictions compared to the earlier ones. Furthermore, a sensitivity analysis of the input variable was also investigated in this study and revealed that the choke size variable had the most significant effect, while the Pwh and GLR showed a slight effect on the liquid rate. Eventually, the leverage approach showed that only 2.1% of the data points were in the suspicious range.

Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches

In the present study, the density and viscosity of the CO2-loaded and –unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.%), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.%), and super activated carbon (SAC) (0.1 wt.%) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.%) +Sulfolane (SFL), 30 wt.%) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15–315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and γ-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 % in high CO2 loading, ∼0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results.

Mechanical Analysis of Semi-Rigid Base Asphalt Pavement under the Influence of Groundwater with the Spectral Element Method

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability.

Processing Maps and Optimized Plastic Behavior Model of TC4 Titanium Alloy Diffusion Bonded Joint at α+β Region Temperatures

The hot processing maps and optimized Arrhenius constitutive model of the TC4 diffusion bonded joint were investigated based on high temperature tensile tests in the temperature range of 1024–1174 K and strain rate range of 0.0001–0.1 s–1. The optimal hot processing parameter in the tensile deformation mode was 0.0001–0.001 s–1/1124 K and 0.0001–0.1 s–1/1174 K, respectively, when the true strain was 0.2. A modified strain compensated Arrhenius-type constitutive model of the joint by combining the evolutionary algorithm and generalized reduced gradient was established. The values of correlation coefficient and average absolute relative error were 0.989 and 7.29%, respectively, indicating the good prediction capabilities.

A deep-learning approach to the 3D reconstruction of dust density and temperature in star-forming regions

Aims. We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2 pc). Methods. We constructed a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 µm. We simplified the task by reconstructing the cloud structure along individual lines of sight (LoSs) and trained a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and it is able to predict full posterior distributions for the target dust properties. We tested different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluated the predictive performance of these models on synthetic test data. Results. We report an excellent reconstruction performance for the 23-wavelength cINN model, achieving median absolute relative errors of about 1.8% in log(n/m−3) and 1% in log(Tdust/K), respectively. We identify trends towards an overestimation at the low end of the density range and towards an underestimation at the high end of both the density and temperature values, which may be related to a bias in the training data. After limiting our coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 2.8% and 1.7% in log(n/m−3) and log(Tdust/K). Conclusions. This proof-of-concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and it is even compatible with a more realistically constrained wavelength coverage.

Impurities removal from acidic solution of nickel cobalt complex hydroxide using pulsed disc and doughnut column

In this work, a 30 mm diameter pulsed disc and doughnut solvent extraction column was used for impurities removal from nickel cobalt complex hydroxide acid solution by solvent extraction. The effects of dispersed phase velocity, continuous phase velocity and pulse intensity on drop diameter, dispersed phase holdup and mass transfer were investigated. The results show that the drop size decreases gradually, and the dispersed phase holdup decreased initially and then increased with the increase of pulse intensity. The dispersed phase holdup increases with the increase of dispersed phase velocity but is independent of the continuous phase velocity. The experimental data of this study are predicted by correlations with regressed parameters. The average absolute relative error of drop size and dispersed phase holdup were 10.07 % and 18.28 %. With the increase of pulse intensity, the extraction efficiency of impurities decreased firstly and then increased. The number of mass transfer units and the height of mass transfer unit were calculated. The trend of number of mass transfer units of impurities are similar to extraction efficiency. Mass transfer results indicate that pulsed disc and doughnut column can be applied to the nickel cobalt complex hydroxide acid solution-P204/kerosene system.

Research on incremental forming simulation based on an improved constitutive model

The incremental sheet metal forming process, characterized by the step-by-step deformation of sheet metal using a simple die, is classified as a type of plastic deformation technology. This method allows for the formation of complex-shaped parts even without the use of a specialized die. Its notable characteristic lies in its low forming load and high flexibility. To accurately forecast finite element simulation outcomes for the incremental sheet forming of 2024-O aluminum alloy sheets, constructing a precise constitutive model is essential. Typically, the uniform strain range in traditional unidirectional tensile tests does not surpass 0.3, primarily due to the necking phenomenon. This limitation hampers the accurate prediction of the actual large plastic deformation process. To address this issue, a theoretical model describing the stress-strain relationship in metal plastic deformation processes is developed using crystal plasticity theory. The constitutive model for the forming of 2024-O aluminum alloy is then constructed by adjusting the hardening coefficient and hardening index. Utilizing the improved constitutive model, the incremental sheet forming process is simulated using ABAQUS software. Upon comparison with experimental results, it is observed that the Thickness Average Absolute Relative Errors (TAARE) of the adjusted constitutive model at forming angles of 60°, 55°, and 50° are 0.414%, 0.467%, and 0.256%, and The Thickness Root Mean Square Errors (TRMSE) are determined as 0.0121, 0.0116, and 0.0094, respectively. These results indicate that the constitutive model and parameters established in this study can adequately capture the mechanical behavior of 2024-O aluminum alloy during the incremental sheet forming process.