Structural Parameters Research Articles

Variations in pore structures and permeabilities of ion adsorption rare earth ores during simulated in-situ leaching: Effect of newly formed clay particles and their swelling

In-situ leaching is essential for mining rare earth elements (REEs) from ion-adsorption rare earth (RE) ores. The efficiency of RE mining is dependent on the permeabilities of ion-adsorption RE ores, and the permeabilities are controlled by their pore structures. However, the current understanding of the pore structures and permeabilities of the RE ores during leaching is limited, particularly their dynamic variations, the controlling roles of different pore structure parameters on the permeability, the roles of the influencing factors and the mechanisms causing the variations. To investigate the above-mentioned issues, we conducted an experimental study of simulated in-situ leaching on undisturbed ion-adsorption RE ore samples under different conditions via constant waterhead permeability tests. The leaching conditions included leaching time, concentration and waterhead of the (NH4)2SO4 solution. Three-dimensional (3-D) pore structures of the RE ores before, during, and after leaching were constructed via X-ray computed tomography, and their pore structure parameters were measured using 3-D image computation. It was observed that the dynamic variations in both the pore structure parameters and permeability coefficients of the RE ores occurred in three distinct stages: rapid reduction, less rapid reduction, and little reduction. The experimental results also revealed that the permeability coefficients of the RE ores were primarily dependent on the average coordination number among the seven pore structure parameters examined, and that the pore throats larger than 30 μm in diameter served as the most effective seepage channels within the RE ores. The waterhead of the leaching solution had a stronger influence on the variations in pore structures and permeabilities compared to that of the concentration. Analysis of the particle/aggregate size distribution and mineralogical compositions of the RE ores before and after leaching indicated that the decreases in the pore structure parameters and permeabilities were largely attributed to clogging of the pore throats and pores by migrated and newly formed clays, as well as swelling of the latter. The newly formed clays were the products of the decomposition of K-feldspar and mica resulting from chemical reactions with the leaching solution. The clays from the two sources occurred in disaggregated and aggregated forms. The results of this study provide an important reference for the mining of RE ores via leaching.

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

Heat transfer enhancement mechanics of different micro-disturbing structures for transcritical hydrocarbon fuel using 3-D secondary flow intensity analysis

Heat transfer enhancement based upon micro-disturbing structures becomes increasingly important for thermal protection of air-breathing propulsion systems. To explore the influence mechanism of different structures on heat transfer in regenerative cooling channels of scramjet engines, flow structures and heat transfer of supercritical-pressure n-Decane in mini-channels with dimple and micro-rib arrays are numerically investigated. Results indicate effective heat transfer enhancement inside the channels depends more on efficient transverse turbulence disturbance rather than longitudinal or perpendicular turbulence disturbance. Severe thermal stratification generates strong shear layers in channels, where transverse secondary flows bring much intenser flow mixing than longitudinal secondary flows, especially in transcritical temperature region where the best heat transfer enhancement is obtained. With same characteristic dimensions, longitudinal micro-disturbing structures like dimples have lower flow resistance, but transverse micro-disturbing structures like micro-ribs achieve stronger heat transfer enhancement. Besides, the performance of micro-ribs is much more sensitive to the dimensional change. Therefore, micro-ribs have larger working excursion for enhancing performance factor than dimples. With optimal structure parameters: hr=e=0.3mm, pr=3mm, hd=0.225mm, dd=1.5mm, overall performance factor in micro-ribbed channel is 1.14 times of that in dimpled channel. Those conclusions provide effective support for optimization design for the regenerative cooling structures.

Influence of corrugated tube structure and flow rate on the hydrogen absorption performance of metal hydride reactor and structural optimization

The metal hydride bed (MHB) reactor is an effective instrument for storing hydrogen. However, thermal stress causes the cooling tube expansion inside the MHB. The long-term effects will lead to the failure of the cooling tube. To absorb the thermal stress and reduce the deformation of the cooling tubes, this article establishes an MHB reactor with corrugated cooling tubes to study its hydrogen absorption performance. The results prove that some parameters are adversarial and flow rate patterns are complex (such as extreme flow rate and insufficient flow rate) in the hydrogen absorption process of the corrugated tube MHB reactor, which makes it impossible to optimize the structure of the corrugated tube through the gradient method. Under the condition of a fixed corrugated tube volume of 3×10−6m3 and a cooling water volumetric flow rate of 0.5×10−4m3/s, the structural parameters of the corrugated tube are optimized by the Monte Carlo algorithm. According to the optimized results (N=8, ro=4.25mm, ri=3.98mm, do=3.17mm, di=4.33mm), the hydrogen absorption efficiency of the corrugated tube MHB reactor (reaction fraction 0.758) is 1.36 times that of the straight tube (reaction fraction 0.557) at 560s. Thus, the corrugated tube can reduce thermal stress and improve the heat transfer effect. This research is beneficial in improving long-term stability, enhancing operational efficiency, and reducing the volume and weight of large MHB reactors.

Galaxies’ properties in the Fundamental Plane across time

Context. Using the Illustris-1 and IllustrisTNG-100 simulations, we investigate the properties of the Fundamental Plane (FP), which is the correlation between the effective radius Re, the effective surface intensity Ie, and the central stellar velocity dispersion σ of galaxies, at different cosmic epochs. Aims. Our aim is to study the properties of galaxies in the FP and its projections across time, adopting samples covering different intervals of mass. We would like to demonstrate that the position of a galaxy in the FP space strongly depends on its degree of evolution, which might be represented by the β and $ L^\prime_0 $ parameters entering the L = $ L^\prime_0 $ (t)σβ(t) law. Methods. Starting from the comparison of the basic relations among the structural parameters of artificial and real galaxies at low redshift, we obtain the fit of the FP and its coefficients at different cosmic epochs for samples of different mass limits. Then, we analyze the dependence of the galaxy position in the FP space as a function of the β parameter and the star formation rate (SFR). Results. We find that: (1) the coefficients of the FP change with the mass range of the galaxy sample; (2) the low luminous and less massive galaxies do not share the same FP of the bright massive galaxies; (3) the scatter around the fitted FP is quite small at any epoch and increases when the mass interval increases; (4) the distribution of galaxies in the FP space strongly depends on the β values (i.e., on the degree of virialization and the star formation rate). Conclusions. The FP is a complex surface that is well approximated by a plane only when galaxies share similar masses and condition of virialization.

The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning

Electrolyte solvents have a critical impact on the design of high performance and safe batteries. Gutmann’s donor number (DN) and acceptor number (AN) values are two important parameters to screen and design superior electrolyte solvents. However, it is more time-consuming and expensive to obtain DN and AN values through experimental measurements. Therefore, it is essential to develop a method to predict DN and AN values. This paper presented the prediction models for DN and AN based on molecular structure descriptors of solvents, using four machine learning algorithms such as CatBoost (Categorical Boosting), GBRT (Gradient Boosting Regression Tree), RF (Random Forest) and RR (Ridge Regression). The results showed that the DN and AN prediction models based on CatBoost algorithm possesses satisfactory prediction ability, with R2 values of the testing set are 0.860 and 0.96. Moreover, the study analyzed the molecular structure parameters that impact DN and AN. The results indicated that TDB02m (3D Topological distance based descriptors - lag 2 weighted by mass) had a significant effect on DN, while HATS1s (leverage-weighted autocorrelation of lag 1 / weighted by I-state) plays an important role in AN. The work provided an efficient approach for accurately predicting DN and AN values, which is useful for screening and designing electrolyte solvents.

Simulation of novel Pt-M nanocatalysis for proton exchange membrane fuel cells

To enhance proton exchange membrane fuel cells, an ultra-thin cathode catalytic layer based on PtPdCu nanowires is analyzed. The purpose is to optimize fuel cell performance by analyzing key parameters of the catalytic layer in detail, such as thickness and porosity. Numerical simulation methods are used to simulate the structural parameters and operating conditions of the catalytic layer using COMSOL Multi-physics software. The paper focuses on analyzing the changes in the transport resistance of electrons, protons, and oxygen within the catalytic layer, as well as the measurement method of the porosity of the catalytic layer. The results demonstrated that when the catalytic layer thickness reached 450 nm, the power density of proton exchange membrane fuel cells reached its peak, which was 801 and 996 mW/cm2, respectively. In catalytic layers with a thickness of less than 1 µm, the transfer efficiency of oxygen and electrons was higher. When the thickness exceeds 5 µm, oxygen transmission was hindered, and the proton transfer path becomes longer. The average porosity was 44.02%, indicating a high structural consistency of the catalytic layer. In terms of redox reaction performance, the area specific activity of PtPdCuNWs was four times that of commercial Pt/C. This study emphasizes the importance of the ultra-thin cathode catalytic layer in optimizing the performance of proton exchange membrane fuel cells and provides insights into improving catalytic efficiency and overall fuel cell performance through micro-structure design.

Excess capacity and hysteresis in EU Countries. A structural approach

We develop a structural method to estimate the rate of capacity utilization in 12 EU countries that consists of identifying the structural parameters of a Leontief production function, using a parsimonious theoretical specification. Parameters are correctly identified, stable and robust to alternative specifications. Our results provide evidence of persistent excess capacity and hysteresis in the rate of capacity utilization in many EU countries, especially after the 2008′s global financial crisis, thereby supporting the claim that larger fiscal deficits would have contributed to absorb excess capacity without producing persistent inflationary pressures or debt unsustainability. Despite the simplicity of our model, the method allows more complex macroeconomic specifications for country-specific applications.

The determination of ADME, toxicity, drug-likeness parameters, and anticancer activity of cis-[Pt(Oro)(NH3)2]

In this study, it was aimed to obtain the anticancer potential, pharmacokinetic and toxicity profiles of the cis-[Pt(oro)(NH3)2]. The structural parameters of the compound were determined. The effects of solvent environments were also investigated on the molecule because they are an important factor that should be taken into account in drug research processes. Pharmacokinetic and toxicity parameters of the molecule were obtained by using in silico predictions, which are good preliminary for in vitro and in vivo studies. The drug absorption, distribution, metabolism, and excretion parameters of cis-[Pt(oro)(NH3)2] were compared with those of the anticancer drug cisplatin. Molecular docking studies on oncogenes were conducted to define the anticancer potential of the molecule. The obtained ADME results showed that the title molecule’s toxicity is significantly lower than cisplatin. Molecular docking studies have revealed that the molecule docks well with the oncogene and thus may be a promising potential anticancer agent.

Heat transfer enhancement mechanics of different micro-disturbing structures for transcritical hydrocarbon fuel using 3-D secondary flow intensity analysis

Heat transfer enhancement based upon micro-disturbing structures becomes increasingly important for thermal protection of air-breathing propulsion systems. To explore the influence mechanism of different structures on heat transfer in regenerative cooling channels of scramjet engines, flow structures and heat transfer of supercritical-pressure n-Decane in mini-channels with dimple and micro-rib arrays are numerically investigated. Results indicate effective heat transfer enhancement inside the channels depends more on efficient transverse turbulence disturbance rather than longitudinal or perpendicular turbulence disturbance. Severe thermal stratification generates strong shear layers in channels, where transverse secondary flows bring much intenser flow mixing than longitudinal secondary flows, especially in transcritical temperature region where the best heat transfer enhancement is obtained. With same characteristic dimensions, longitudinal micro-disturbing structures like dimples have lower flow resistance, but transverse micro-disturbing structures like micro-ribs achieve stronger heat transfer enhancement. Besides, the performance of micro-ribs is much more sensitive to the dimensional change. Therefore, micro-ribs have larger working excursion for enhancing performance factor than dimples. With optimal structure parameters: hr = e = 0.3 mm, pr = 3 mm, hd = 0.225 mm, dd = 1.5 mm, overall performance factor in micro-ribbed channel is 1.14 times of that in dimpled channel. Those conclusions provide effective support for optimization design for the regenerative cooling structures.

The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning

Electrolyte solvents have a critical impact on the design of high performance and safe batteries. Gutmann’s donor number (DN) and acceptor number (AN) values are two important parameters to screen and design superior electrolyte solvents. However, it is more time-consuming and expensive to obtain DN and AN values through experimental measurements. Therefore, it is essential to develop a method to predict DN and AN values. This paper presented the prediction models for DN and AN based on molecular structure descriptors of solvents, using four machine learning algorithms such as CatBoost (Categorical Boosting), GBRT (Gradient Boosting Regression Tree), RF (Random Forest) and RR (Ridge Regression). The results showed that the DN and AN prediction models based on CatBoost algorithm possesses satisfactory prediction ability, with R2 values of the testing set are 0.860 and 0.96. Moreover, the study analyzed the molecular structure parameters that impact DN and AN. The results indicated that TDB02m (3D Topological distance based descriptors - lag 2 weighted by mass) had a significant effect on DN, while HATS1s (leverage-weighted autocorrelation of lag 1 / weighted by I-state) plays an important role in AN. The work provided an efficient approach for accurately predicting DN and AN values, which is useful for screening and designing electrolyte solvents.

Simulation calculation and optimization of hydraulic buffer device based on Simulink

Abstract When the hydraulic cylinder moves at high speed to the end of the stroke, it usually produces severe pressure, shock, and vibration, and makes the system very unstable, so the buffer device is often used to buffer the deceleration process of the hydraulic cylinder. In this paper, the cushioning process of a high-pressure hydraulic cylinder buffer device is modeled mathematically, the motion parameters in the cushioning process are calculated based on Simulink, and different structural parameters of the cylinder block are optimized and compared, to study the influence of structural parameters of the cushioning device on the cushioning process.

CFD modeling and optimal design of louvered fins heat exchangers using radical basis function

Louvered fins heat exchanger is widely applied to chemical, energy, and refrigeration industries. The manuscript proposes a way for accurately and efficiently optimizing louvered fins heat exchangers. The louvered fin pitch, louvered fin height and louvered fin length are selected as main independent variable. The optimal structural parameters were extracted by CFD simulation, RBF and MOGA. The Colborn coefficient j and resistance coefficient f need to be optimized to be optimal. Using radical basis function (RBF) to find out approximation model, and the structure size is optimized by multi-objective genetic algorithm (MOGA). It shows that Colborn coefficient j reduces by 4 %, and resistance coefficient f reduces by 43 %. And the convection heat transfer is basically unchanged, when the flow resistent index is obviously reduced. Secondly, the effect of the optimized structure is analyzed in terms of pressure, velocity and temperature, and the optimization effect is further emphasized. Finally, the good performance of the optimization structure is further verified by the field synergy theory.

Exploring High-Performance Photonic Crystal Slow Light Waveguides through Deep Reinforcement Learning

Supervised deep learning is not really suitable for automatic inverse design of photonic crystal waveguides. There are three reasons for this:(1) Pre-training of the network requires a large number of datasets, which leads to a lot of time spent in the physical simulation process. (2) The one-to-many mapping problem often arises during the reverse design process. (3) It is difficult to realize the inverse design of photonic structures beyond the performance of the dataset. These problems can be well addressed by deep reinforcement learning (DRL). Here, deep reinforcement learning is proposed for the first time for the design of complex slow-light photonic crystal waveguides. The design using DRL took less than 72 hours to obtain a photonic crystal waveguide with a normalized delay bandwidth product of 0.449 and group index of 48.72. By increasing the scaling factor of bandwidth in the reward function to 0.25, the bandwidth value of the optimized slow-light waveguide increases to 50.01 nm with an NDBP value of 0.39. The slow-light performance of both structures is verified by FDTD. In addition, the NDBP value is further optimized to 0.46 if the size of the structural parameters is not constrained. Compared with deep learning, DRL can improve the sampling efficiency by more than an order of magnitude and is not plagued by the three problems mentioned above. This work confirms the effectiveness of DRL in the inverse design of energy band engineering. DRL can be widely used in the optimization of energy band related performance.

Novel Ternary Halide Perovskites - CaKI3 and CaRbI3 : Investigation of structural, electronic and optical properties for optoelectronic applications.

The full-potential linearized augmented plane wave approach is used in this work to investigate the structural, electronic, elastic, thermal, and optical properties of CaKI3 and CaRbI3 cubic perovskite compounds using GGA_PBE and TB_mBJ approximations. The structures have been relaxed and ground state structural parameters are calculated. Density of states calculated reveal narrow band gaps which are around Schockley-Queisser limit(Eg≈1.34 eV). Optical characteristics imply that CaKI3 may be more appropriate for solar cell applications than CaRbI3.

High-performance gas sensing in metagrating-based vertical cavity with larger fabrication tolerances driven by quasi-bound states in the continuum

We proposed a novel vertical cavity structure for gas sensing, utilizing double-layer metagratings and a distributed Bragg reflector (DBR). By adjusting the position of the upper-layer grating, we were able to achieve symmetry-protected bound states in the continuum (SP-BIC) and Fabry-Pérot BIC (FP-BIC). The resonance peaks of these quasi-BICs can be tuned by modifying structural parameters like grating thickness, width, and cavity length. Notably, our structure exhibited high sensing performance while allowing for significant fabrication tolerances. Simulation results indicate that the sensitivities of the gas sensor can reach 927 nm/RIU and 1026 nm/RIU, corresponding to figure of merit (FOM) of 15450 RIU-1 and 24604 RIU-1. The proposed structure with large fabrication tolerances has potential applications in gas sensing.

Tunable plasmonic tweezers based on nanocavity array structure for multi-site nanoscale particles trapping

The ability of plasmonic optical tweezers based on metal nanostructure to stably trap and dynamically manipulate nanoscale objects at low laser power has been widely used in the fields of nanotechnology and life sciences. In particular, their plasmonic nanocavity structure can improve the local field intensity and trap depth by confining electromagnetic fields to subwavelength volumes. In this paper, the R6G dye molecules with 10−6 M were successfully trapped by using the Ag@Polydimethylsiloxane nanocavity array structure, and a R6G micro-ring was formed under the combined action of plasmonic optical force and thermophoresis. Subsequently, the theoretical investigation revealed that the trapping performance can be flexibly adjusted by changing the structural parameters of the conical nanocavity unit, and it can provide a stable potential well for polystyrene particles of RNP = 14 nm when the cavity depth is 140 nm. In addition, it is found that multiple trapping sites can be activated simultaneously in the laser irradiation area by investigating the trapping properties of the hexagonal conical nanocavity array structure. This multi-site stable trapping platform makes it possible to analyze multiple target particles contemporaneously.

Electrochemical performance of cauliflower like binder-free magnesium-sulphide-electrodes for supercapacitor application

Herein, magnesium sulphide (MgS) electrode material (EM) is deposited on nickel-foam (NF) placed at 16 and 17 cm away from the raw material by simple powder vapour transport method. The increasing source to substrate distance (SSD) not only changed the structural parameters but also changed the nanofibers to cauliflowers like surface morphology. The cauliflowers like MgS/NF EM exhibited high specific capacitance (6339 F/g) as compared to the specific capacitance (1677 F/g) of nanofibers like MgS/NF EM. The cauliflowers like EM is showed high energy density of 220–99 Wh/kg, power density of 250–2500 W/kg, equivalent series resistance of 0.95 O and capacitance-stability of 93 % even for 10,000 cycles. The increasing SSD is fruitful for the increment in diffusion rate as confirmed by shifting of electrochemical impedance spectrum to y-axis, Dunn’s model and power law simulations. The diffusive contribution is 88/82 % at 5 mV/s which become 63/50 % at 100 mV/s for the cauliflower/nanofibers like EM. Moreover, the cauliflowers like EM have both battery and capacitive nature as 0.5 < b < 1 predicted by power law simulations. Additionally, the configured asymmetric supercapacitive (ASC) device is presented the specific capacitance of 834 F/g, energy density of 354–247 Wh/kg and power density of 2625–26250 W/kg. The ASC device could maintain the Coulombic-efficiency of 99 % and capacitance stability of 93 % even for 10,000 cycles. The excellent pseudocapacitive behaviour of MgS/NF EM enables them to use in the lithium ion battery and supercapacitor devices.

Numerical validation of a novel cuttings bed impeller for extended reach horizontal wells

To reduce the risk of downhole accidents resulting from poor wellbore cleaning during the drilling of extended reach horizontal wells, a new cuttings bed impeller is developed. The performance of existing cuttings bed impellers on wellbore cleaning efficiency is analyzed by multiphase numerical simulation. Based on this analysis, a new type of cuttings bed impeller is proposed, and its key structure parameters are optimized. Its cuttings removal performance under different working conditions is verified. The results show that the spiral impellers have the highest annular velocity, promoting the movement of the cuttings bed. Increasing the rotation speed of the impeller causes the cuttings bed to move further from the low side of the wellbore, facilitating the cuttings removal. Two major improvements are introduced to the new cuttings bed impeller: a positive displacement motor that enables the self-rotation of the impeller, and the elastic contacts on the spiral blades that stir cuttings bed and reduce friction with the wellbore. The optimized parameters of the new impeller are: helix angle of 60°, 4 blades, elastic contacts arranged in crossed pattern, and self-rotation speed of 60 r/min. It is also demonstrated that the new impeller achieves satisfactory cleaning results in both rotary drilling (84.71%) and sliding drilling (71.07%) conditions. This work provides a new solution for the efficient removal of cuttings in extended reach horizontal wells.

Optimization and thermal characterization of a new liquid-cooled plate with branching channels of fractal geometry

In order to improve the heat transfer effect of the liquid-cooled plate with the traditional straight channel, 24 new liquid-cooled structures were obtained based on the fractal concept by adding 6 geometrically structured branch channels to the traditional straight channel and adding different forms of secondary channels between the branch channels and between the branch channels and the main channel. On the basis of establishing a numerical simulation model and verifying the accuracy of the model, the thermal characteristics of 24 liquid-cooled structures were analysed by computational fluid dynamics (CFD) simulation, and the initial relative optimal liquid-cooled channel structure was obtained by taking the comprehensive thermal performance as the evaluation index. Then, the initial relative optimal channel structure parameters were optimised in steps using NSGA-II and grey relational analysis algorithms. In order to further improve the heat transfer effect of the liquid-cooled plate, the relative optimal model after stepwise optimization was arced. The final results showed a decrease in the average temperature by 0.57 °C (1.6 %), an increase in the heat transfer coefficient by 356 W m−2 k−1 (21.3 %), and a decrease in the pressure drop by 34.55 Pa (50.2 %), as compared with the initial model. This study will provide some reference for the optimisation of the internal structural features of the channel and the improvement of the thermal performance of the liquid-cooled plate.