Compressible Model Research Articles

Electroacupuncture alleviates neuropathic pain in a rat model of CCD via suppressing P2X3 expression in dorsal root ganglia

Abstract Background Sciatica and low back pain are prevalent clinical types of neuropathic pain that significantly impair patients' quality of life. Conventional therapies often lack effectiveness, making these conditions challenging to treat. Electroacupuncture (EA) is an effective physiotherapy for pain relief. Prior research has demonstrated a relationship between the frequency of neuropathic pain and the analgesic impact of EA stimulation. This work aimed to assess the analgesic effects of EA in a rat model of chronic compression of the dorsal root ganglion (CCD) and to understand the underlying processes. Methods We established a rat CCD model to simulate sciatica and low back pain. EA was applied to rats with CCD at various frequencies (2 Hz, 100 Hz, and 2/100 Hz). The paw withdrawal threshold (PWT) was measured to assess analgesic effects. Additionally, protein levels of the purinergic receptor P2X3 (P2X3) and the expression of nociceptive neuronal markers were analyzed using immunohistochemistry and western blot (WB) techniques. The study also measured levels of proinflammatory cytokines TNF-α and IL-1β in the dorsal root ganglion (DRG). The involvement of P2X3 receptors was further investigated using the P2X3 agonist, α,β-methylene ATP (α,β-meATP). Results CCD rats developed pronounced mechanical allodynia. EA stimulation at all tested frequencies produced analgesic effects, with 2/100 Hz showing superior efficacy compared to 2 Hz and 100 Hz. The expression of P2X3 was increased in ipsilateral DRG of CCD model rats. P2X3 were co-labeled with isolectin B4 (IB4) and transient receptor potential vanilloid (TRPV1), indicating their role in nociception. 2/100 Hz EA treatment significantly reduced mechanical allodynia and inhibited the overexpression of P2X3, TRPV1, substance P (SP), and calcitonin gene-related peptide (CGRP) in the ipsilateral DRG of CCD model rats. Additionally, EA reduced the levels of proinflammatory cytokines TNF-α and IL-1β in the ipsilateral DRG, indicating an anti-inflammatory effect. The P2X3 agonist α,β-me ATP attenuated the analgesic effect of 2/100 Hz EA in CCD rats. The WB and immunofluorescence results consistently demonstrated P2X3 inhibition contributed to the analgesic effects of 2/100 Hz EA on CCD-induced neuropathic pain. Conclusions Our findings suggest that 2/100 Hz EA alleviates neuropathic pain in rats by inhibiting the upregulation of P2X3 receptors in the ipsilateral DRG. This study backs up EA as a viable treatment option for sciatica and low back pain in clinical settings.

Theoretical model construction and static performance analysis of multi-leaf journal foil bearings

Abstract Multi-leaf journal foil bearing (MLJFB) structures are complex, making performance prediction challenging, and the accurate prediction of gas film thickness and pressure distributions is crucial. In this study, COMSOL finite-element software was used to establish an initial foil assembly and structural deformation simulation model for an MLJFB with bump foils. Using MATLAB, a compressible gas lubrication model was established and solved using the finite volume method (FVM). Real-time data transfer between COMSOL and MATLAB enabled a fully coupled fluid-structure interaction simulation. This model meticulously accounts for the pre-tension force in the multi-leaf bearing assembly, contact friction between adjacent foils, and the interaction between the top foil and gas film pressure. The effects of journal movement distance, direction, and top foil overlapping ratio on the bearing capacity as well as the effects of different external load directions on the journal equilibrium position were analyzed based on this model. The results indicate that increasing the top foil overlapping ratio enhances the bearing capacity of the MLJFB. Furthermore, compared to the direction of the top foil overlap position, external loads applied in the direction of the top foil midpoint are more beneficial for bearing operational stability.

Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks

Focusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidifying agent ratio, water content, and curing age, based on experimental data accumulated during years of engineering practice and an artificial neural network. In this study, a backpropagation (BP) neural network is adopted, and 114 sets of experimental data are used to train the parameters of the unconfined compressive strength prediction model. Then, using the BP strength prediction model, the material scheme optimization process is carried out, with the strength and material costs as the objectives. The results show that the BP neural network model has a high prediction accuracy, the relative prediction error is basically within 10%, the root-mean-squared error is less than 0.04, and the correlation coefficient is more than 0.99. According to the strength requirements of modified red mud in different road projects and the constraints of each property, an optimal material scheme with a lower cost and higher 7 d target strength is obtained using a mix of polymer agent–fly-ash–cement–speed-cement in a ratio of 0.02%:1.96%:4.78%:0%, with a 33.93% water content of raw red mud, so that the target strength and material cost are 2.987 MPa and 17.099 CNY/T. This study creates an optimal material scheme, incorporating the compound-solidifying agent ratio, curing age, and water content of the modified red mud road material according to the strength requirements of different projects, thereby promoting the popularization of the utilization of red mud with better engineering practicability and economy.

What can be observed in intervertebral cartilage endplate with aging? An animal model study of excessive axial mechanical loading

IntroductionThe cartilage endplate (CEP) plays a crucial role as both a mechanical barrier and nutrient channel for the intervertebral disc, but it is vulnerable to excessive axial loading. We modified the Ilizarov external fixator and applied it to the CEP of the rat tail to impose diurnal, controllable excess axial loading. The objective was to measure morphological changes in the CEP when subjected to loading during the aging process.MethodsTwo Kirschner wires were, respectively, inserted into the center of the eighth and ninth coccygeal vertebrae (Co8/9) of rat (n = 54) to apply axial loading to the CEP. A remote control device was used to establish the diurnal loading schedule. At the end of 4, 8, and 12-week periods, the Co8/9 CEPs in each group were analyzed using MRI, histological staining, and immunohistochemical staining techniques.ResultsThe novel Ilizarov model that we modified successfully induced degeneration of the rat coccygeal CEP. MRI analysis revealed significant degenerative changes in the loaded Co8/9 CEP, including decreased signal intensity and the formation of Schmorl’s nodes at 8 and 12 weeks. Histological examination showed progressive CEP degeneration (CEPD), characterized by decreased microporosity, thinning, and structural irregularities. Immunohistochemical analysis demonstrated a significant reduction in Aggrecan and Collagen II expression in the CEP and nucleus pulposus over time. Control and sham groups maintained normal CEP structure and composition throughout the study period.ConclusionExcessive axial loading induced CEPD in the rat tail, primarily characterized by the formation of Schmorl’s nodes and a reduction in CEP microporosity in this study. Our modified Ilizarov rat tail compression model, featuring stable and controllable axial loading capabilities, provided an alternative experimental paradigm for further investigation into CEPD.

Failure criterion and compressive constitutive model of seawater concrete incorporating coral aggregate subjected to biaxial loading

Failure criterion and compressive constitutive model of seawater concrete incorporating coral aggregate subjected to biaxial loading

Intelligent Fault Diagnosis Method Based on Neural Network Compression for Rolling Bearings

Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring operation of machinery, and it has been facilitated by the growing popularity of convolutional neural networks (CNNs). The outstanding performance of fault diagnosis CNNs results from complex and redundant network structures and parameters, resulting in huge storage and computational requirements, which makes it challenging to implement these models in resource-limited industrial devices. This study aims to address this problem by proposing a comprehensive compression method for CNNs that is applied to intelligent fault diagnosis. It involves several different compression methods, including tensor train decomposition, parameter quantization, and knowledge distillation for deep network compression. This results in a significant decrease in redundancy and speeding up the training of CNN models. Firstly, tensor train decomposition is applied to reduce redundant connections in both convolutional and fully connected layers. The next step is to perform parameter quantization to minimize the bits needed for parameter representation and storage. Finally, knowledge distillation is used to restore accuracy to the compressed model. The effectiveness of the proposed approach is confirmed by an experiment and ablation study with different models on several datasets. The results show that it can significantly reduce redundant information and floating-point operations with little degradation in accuracy. Notably, on the CWRU dataset, with about 60% parameter reduction, there is no degradation in our model’s accuracy. The proposed approach is a new attempt at the intelligent fault diagnosis of rolling bearings in industrial equipment.

Calculation model of shale fracture compressibility and evolution of permeability under water-bearing conditions

Water saturation of shale reservoirs significantly influences the permeability and compressibility of propped fractures. This study focused on the Longmaxi Formation shale reservoir in northern Guizhou, China, where the permeability of water–saturated shale under varying gas and confining pressures was measured. A compressibility model for proppant embedment and compaction deformation was developed and validated against the experimental results. This study examined the compressibility of supported fractures considering water–rock interactions and elucidated the intrinsic relationship between compressibility and water saturation. The findings demonstrated a decreased trend in shale fracture permeability with increasing water saturation under identical conditions. Compared to dry shale, the permeability decreased by 1.2%–16.4% and 2.0%–17.8% at water saturation of 15% and 50%, respectively. The results of the model calculations demonstrate that fracture compressibility is contingent on the degree of variation of the fracture width. Prolonged water–rock interactions intensified the variation in the fracture width increasing the compressibility under the same stress conditions. As the water saturation increased from 0% to 50%, the fracture closure rate increased from 0.034 to 0.179 with the increase in effective stress. Increased water saturation also increases the sensitivity of the fracture compressibility to effective stress while decreasing the elastic modulus of the rock, thereby enhancing the proppant embedment depth and significantly increasing the fracture compressibility. This study provides critical insights into the dynamic evolution of fracture permeability during hydraulic fracturing and offers valuable implications for gas production forecasting.

Numerical investigation on the liquid jet and the dynamics of the near-wall cavitation bubble under an acoustic field

The dynamics of the near-wall cavitation bubble in an acoustic field are the fundamental forms of acoustic cavitation, which has been associated with promising applications in ultrasonic cleaning, chemical engineering, and food processing. However, the potential physical mechanisms for acoustic cavitation-induced surface cleaning have not been fully elucidated. The dynamics of an ultrasonically driven near-wall cavitation bubble are numerically investigated by employing a compressible two-phase model implemented in OpenFOAM. The corresponding validation of the current model containing the acoustic field was performed by comparison with experimental and state-of-the-art theoretical results. Compared to the state without the acoustic field, the acoustic field can enhance the near-wall bubble collapse due to its stretching effect, causing higher jet velocities and shorter collapse intervals. The jet velocity in the acoustic field increases by 80.2%, and the collapse time reduces by 40.9% compared to those without an acoustic field for γ = 1.1. In addition, the effects of the stand-off distances (γ), acoustic pressure wave frequency (f), and initial pressure (p*) on the bubble dynamic behaviors were analyzed in depth. The results indicate that cavitation effects (e.g., pressure loads at the wall center and the maximal bubble temperature) are weakened with the increase in the frequency (f) owing to the shorter oscillation periods. Furthermore, the maximum radius of bubble expansion and the collapse time decrease with increasing f and increase with increasing p*. The bubble maximum radius reduces by 12.6% when f increases by 62.5% and increases by 20.5% when p* increases by 74%.

Experimental Study on Mechanical Properties and Stability of Marine Dredged Mud with Improvement by Waste Steel Slag

As marine-dredged mud and waste steel slag in coastal port cities continue to soar, the traditional treatment method of land stockpiling has caused ecological problems. Thus, it is necessary to find a large-scale resource-comprehensive utilization method for dredged mud and waste steel slag. This study uses waste steel slag and composite solidifying agents (cement, lime, fly ash) to physically and chemically improve marine-dredged mud. The physical improvement effect of the particle size and dosage of waste steel slag was studied by the shear strength test under the effect of freeze–thaw cycle. Then, based on the Box–Behnken design of the response surface method, the interaction effects of the solidifying agent components on the unconfined compressive strength were studied. Then, the water stability under dry–wet cycles and a microscopic mechanism were analyzed by XRD and SEM tests. The results show that the waste steel slag with a dosage of 30% and a particle size of 1.18~2.36 mm has the best improvement. The interaction between cement and lime and lime and fly ash has a significant effect on the linear effect and surface effect of 7d unconfined compressive strength, and the strength increases first and then decreases with the increase in its dosage. For the 14d unconfined compressive strength, only the interaction between cement and lime is still significant. The unconfined compressive strength prediction model is established to optimize the mix ratio of the composite solidifying agent. In the water stability, the water stability coefficients of the 7d and 14d tests are 0.68 and 0.95, respectively, and the volume and mass loss rates are all below 1.5%, showing a good performance in dry–wet resistance and durability. Microscopic mechanism analysis shows that waste steel slag provides an ‘anchoring surface’ as a skeleton, which improves the pore structure of dredged mud, and the hydration products generated by the solidifying agent play a role in filling and cementation. The results of the study can provide an experimental and technical basis for the resource engineering of marine-dredged mud and waste steel slag, helping the construction of green low-carbon and resource-saving ports.

Study on the strength influence mechanism and cracking mechanism of stone powder-cement floor grouting materials

Underground grouting is a concealed project, and it is difficult to test the strength of grouting stones in engineering practice. Aiming at the problems of high confined water pressure and strong water-richness of the roadway floor, a pressure filtration test device was developed, and a PFC2D uniaxial compression model was established to study the variation law of stone strength and crack evolution mechanism of grouting materials under different grouting pressures. The results show that with the increase of pressure filtration value, the amount of slurry dehydration increases, the pore water pressure dissipates, the pores between particles are tightly compressed, the contact force between particle skeletons increases, and the strength of stone body increases. Under the condition of the same cement powder ratio (mass ratio of cement to stone powder) or stone powder particle size, the strength of stone body of stone powder-cement grouting material increases with the increase of pressure filtration value. The stress-strain curves of the stone body with different pressure filtration values all experienced three stages: continuous elasticity, fracture expansion and strength failure. Before the peak, the number of cracks increases slowly; after reaching the peak, the micro-cracks extend rapidly and the number increases rapidly, resulting in the final tensile failure of the specimen. This study can provide a basis for the selection of grouting engineering material ratio and grouting pressure parameters.

Simulation study on the cavitation flow characteristics of liquid ammonia in the nozzle under high-pressure injection conditions

In this study, a non-isothermal compressible cavitating flow simulation model for liquid ammonia in high-pressure injector, accounting for the phase change properties, was established. The model was validated using experimental data from existing literature. The cavitation phase change process of liquid ammonia within the injector nozzle was investigated, and the differences in cavitation distribution between ammonia and diesel were explored. The results indicate that with increasing injection pressure, the vapor volume fraction of ammonia increases linearly, while that of diesel fuel shows a rapid initial rise followed by a decelerating trend. In terms of flow coefficient, ammonia is lower than diesel, though the two values converge at higher injection pressures. Under low injection pressure, the heat absorbed by the cavitation phase change of ammonia exceeded the heat generated by the high-speed friction of ammonia flow against the nozzle wall, leading to a temperature drop at the nozzle inlet. Conversely, at high injection pressure, the temperature of ammonia on the nozzle wall increases by more than 20 K, resulting in a rapid rise in saturated vapor pressure and significantly intensifying the cavitation effect. Compared to ammonia, diesel experienced a higher temperature rise within the nozzle but exhibited smaller variations in saturated vapor pressure, making the temperature effect on cavitation less pronounced. Diesel’s higher density and viscosity resulted in lower flow velocities compared to ammonia, leading to more developed cavitation in the lower wall region of the nozzle and creating a higher pressure region on the upper wall of the nozzle, which was more distant from the nozzle inlet and inhibited cavitation development there. The research results provide theoretical support for the design of liquid ammonia injectors.

Unified uniaxial compressive constitutive model for C20–C120 concrete based on stochastic damage theory

The concrete uniaxial compressive stress-strain model is essential for analyzing reinforced concrete members and structures. However, models applicable to C20–C120 concrete are still relatively rare. Moreover, the previous empirical model construction methods may not reflect the physical mechanisms and randomness of concrete failure well. To this end, the database was constructed by collecting and analyzing uniaxial compressive stress-strain curve test results from different scholars. The cubic compressive strength in the database ranges from 9.9 MPa to 137.3 MPa. The adaptability of the stochastic damage model was validated using the database of test curves. Subsequently, stochastic damage models for C20–C120 concrete were calibrated based on concrete compressive strength grouping curves, and practical analysis models were suggested. The results indicate that the calculated curves closely match the test curves when the plastic strain evolution parameters η1,c and η2,c in the stochastic models are taken as 0.001–0.30 and 0.19–0.25, respectively. The adaptability of the stochastic damage model is satisfactory, but the plastic strain evolution laws vary for different concrete compressive strengths, influenced by mix proportions and the probability of failure of each component on the meso-scale. When η1,c is calculated by the empirical formula proposed in this paper and η2,c is approximated as 0.22, the calculated curves for C20–C120 concrete are consistent with the mean values of the test curves. The proposed practical analysis models provide comparable accuracy to stochastic damage models, eliminating the need for iterative calculations, making them convenient for engineering applications.

Swelling Induced Hardening and Degradation Induced Slow-Expansion Hydrogel for a Modified Cervical Spinal Cord Compression Animal Model.

This study presents the development of a polyurethane hydrogel (PUG) for use in a chronic cervical spinal cord compression animal model, leveraging microphase separation and dynamic covalent bonds to achieve swelling induced hardening and degradation-induced slow expansion. PUG-SS and PUG-SS-60% were synthesized with varying disulfide bond concentrations, offering controllable degradation rates and mechanical properties. The hydrogels demonstrated significant swelling-induced hardening and maintained compression above the cervical spinal cord's intrinsic modulus. MRI and histopathological analyses confirmed effective and sustained spinal cord compression, with PUG-SS-60% showing prolonged effects. Behavioral tests, including the BBB locomotor scale, von Frey pain test, and catwalk gait analysis, indicated quicker motor function recovery with PUG-SS and sustained compression with PUG-SS-60%. In vitro cytotoxicity assays showed no significant hydrogel-induced cell death. This study underscores the potential of PUG-SS-60% for providing controlled, sustained compression in chronic spinal cord compression models, paving the way for advanced nonsurgical treatment strategies and improved understanding of degenerative cervical myelopathy (DCM) pathology.

On Elastic Language Models

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. While their large scales ensure capacity, they also hinder deployment. Knowledge distillation offers an opportunity to compress a large language model to a small one, in order to reach a reasonable latency-performance tradeoff. However, for scenarios where the number of requests (e.g., queries submitted to a search engine) is highly variant, the static tradeoff attained by the compressed language model might not always fit. Once a model is assigned with a static tradeoff, it could be inadequate in that the latency is too high when the number of requests is large, or the performance is too low when the number of requests is small. To this end, we propose an elastic language model ( ElasticLM ) that elastically adjusts the tradeoff according to the request stream. The basic idea is to introduce a compute elasticity to the compressed language model, so that the tradeoff could vary on-the-fly along a scalable and controllable compute. Specifically, we impose an elastic structure to equip ElasticLM with compute elasticity and design an elastic optimization method to learn ElasticLM under compute elasticity. To serve ElasticLM , we apply an elastic schedule. Considering the specificity of information retrieval, we adapt ElasticLM to dense retrieval and reranking, and present an ElasticDenser and an ElasticRanker, respectively. Offline evaluation is conducted on a language understanding benchmark GLUE, and several information retrieval tasks including Natural Question, Trivia QA and MS MARCO. The results show that ElasticLM along with ElasticDenser and ElasticRanker can perform correctly and competitively compared with an array of static baselines. Furthermore, an online simulation with concurrency is also carried out. The results demonstrate that ElasticLM can provide elastic tradeoffs with respect to varying request stream.

Effects of Structure Parameters on Static Performance of Gas Foil Bearings Based on a New Fully Coupled Elastic–Aerodynamic Model

Abstract: Accurately predicting the performance of gas foil bearings (GFBs) is important. This paper presents a new fully coupled elastic–aerodynamic model for analyzing the static performance of gas foil journal bearings (GFJBs). The gas compressible lubrication model was solved in MATLAB to obtain the gas pressure. The foil structure deformation was solved in COMSOL by considering the Coulomb friction and allowing the contact surfaces to be separated from each other. Under given load and rotational speed conditions, the calculated minimum gas film thickness and attitude angle match well with the literature data, validating the accuracy of the developed model. Based on the model developed, a comprehensive and systematic analysis of the effects of the structural parameters on the static performance was performed. The results showed that as the bump height, top foil thickness, and bump foil thickness increased, the load capacity could be improved to different degrees. The bump foil thickness had the greatest effect on the load capacity. These results provide theoretical guidance for the structural design and practical applications of GFJBs.

Dynamic Constitutive Model of Tungsten‐Whisker‐Reinforced Aluminum/Polytetrafluoroethylene Composite Material and Quantitative Determination of Impact Reaction Release Energy in Vacuum Environment

To meet the strength requirements of aluminum/polytetrafluoroethylene (Al/PTFE) in practical applications, tungsten (W) whisker is added into the traditional formula Al/PTFE (26.5%/73.5%) to improve the dynamic compressive strength of the reactive material. Mechanical properties of reactive material specimens prepared by sintering are tested. In the results, it is shown that the dynamic compressive strength of Al/PTFE‐reactive material reinforced by tungsten whisker with volume fraction of 13.1% can reach 141 MPa at strain rate of 1800 s−1. The constitutive model of dynamic compression for tungsten‐whisker‐enhanced Al/PTFE‐reactive material is constructed, and the results are basically consistent with the experimental results. The quantitative impact energy release of reactive material specimens under vacuum environment are performed by using the two‐stage light gas gun loading system, and the quantitative impact release energies of Al/PTFE‐reactive material reinforced by tungsten whisker under different whisker percentage content and diameter are obtained.

A Strain-Controlled Finite Strain Model for CRD Consolidation of Saturated Clays Considering Non-Linear Compression and Permeability Relationships

Consolidation is the combined phenomenon of the compression and groundwater seepage of clay. Accurate evaluation of the consolidation characteristic is essential for the design, construction, and long-term stability of geotechnical structures. In this study, a strain-controlled non-linear finite strain model for constant rate-of-deformation (CRD) consolidation was developed for quickly and reliably predicting the consolidation behavior of clay soils. The model can account for any form of non-linear compression and permeability relationships, thus considering variations in the coefficient of consolidation. Being strain-controlled, it overcomes the limitations of stress-controlled models which require complex numerical iteration. The validity and accuracy of this model were verified through rigorous comparisons with both numerical simulations and experimental data. For normally consolidated soils, a non-linear e-lgσ′compression model was used instead of a linear compression model. For overconsolidated soils, the Harris function compression model was determined to be recommended to overcome the discontinuities in total stress and pore pressure caused by the traditional piecewise e-lgσ′ model. It was also found that determining the steady state of consolidation for normally consolidated soils should use the non-linear method, while the linear method is suggested to be adopted for overconsolidated soils.

Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s

Improving the accuracy and detection speed of bolt recognition under the complex background of the train underframe is crucial for the safety of train operation. To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. The underframe bolt images are captured by a self-designed track-based inspection robot, and a dataset is constructed by mixing simulated platform images with real train underframe bolt images. By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. It is compared with FasterNet, GhostNet, and MobileNetV3. Additionally, the CA attention mechanism is introduced, and MPDIoU is used as the loss function of YOLOv8s. LAMP scores are used to rank the model weight parameters, and unimportant weight parameters are pruned to achieve model compression. The compressed SFCA-YOLOv8s model is compared with models such as YOLOv5s, YOLOv7, and YOLOX-s in comparative experiments. The results indicate that the final model achieves an average detection accuracy of 93.3% on the mixed dataset, with a detection speed of 261 FPS. Compared with other classical deep learning models, the improved model demonstrates superior performance in detection effectiveness, robustness, and generalization. Even in the absence of sufficient real underframe bolt images, the algorithm enables the trained network to better adapt to real environments, improving bolt recognition accuracy and detection speed, thus providing technical references and theoretical support for subsequent related research.

Kinetic characteristics of bornite and chalcopyrite dissolution in nitric acid

The kinetic characteristics of dissolution of copper-bearing sulfides – chalcopyrite (CuFeS₂) and bornite (Cu₅FeS₄) – in nitric acid were studied. The kinetics of the dissolution process was described using a compressible nucleus model. Chalcopyrite of the Vorontsovskoye deposit and bornite of the Karabash deposit were used as research objects. Solution and cake samples were analyzed by optical emission spectrometry and X-ray fluorescence analysis, respectively. The results obtained were processed in the MS Excel software package. The influence of various factors, including temperature, solvent concentration, particle size, and process duration on the dissolution degree of minerals was studied. The process parameters were varied as follows: temperature – from 35 to 95°C; HNO₃ concentration – from 1 to 9 mol/dm³; particle size – from +0.1 to 0.056 mm; duration – from 0 to 60 min. It was established that an increase in temperature and acid concentration leads to a significant increase in the degree of dissolution of both chalcopyrite and bornite. A decrease in particle size also contributes to a more efficient dissolution of both minerals in nitric acid. The calculated activation energy values were 55 kJ/mol for chalcopyrite and 43 kJ/mol for bornite, which is characteristic of the kinetic region of the process. The reaction orders in terms of reactant were determined: 1.62 for chalcopyrite and 1.57 for bornite. In terms of particle size, these were -1.16 for chalcopyrite and -2.53 for bornite. On this basis, generalized equations of dissolution kinetics for both minerals were derived. The results obtained allow an assumption about the kinetic nature of dissolution of chalcopyrite and bornite under the studied conditions.

Performance of unreinforced and geogrid-reinforced pile-supported embankments under localized surface loading: Analytical investigation

An analytical solution is proposed to identify the performance of unreinforced and geogrid-reinforcement pile-supported embankments under localized surface loading at working stress conditions based on the total efficacy and efficacy induced by soil arching alone, average strain of geogrid reinforcement, and average settlement of subsoil. This solution considered interactively soil arching within the embankment fill, the load-deflection behavior of geogrid reinforcement (if existed), and subsoil settlement. Specifically, the soil arching consisted of a structural arch with different stress states (evaluated by the elastoplastic state coefficient K) and a frictional arch. The load-deflection behavior of geogrid reinforcement was modeled by a membrane, with due consideration of the skin friction between the geogrid and soil. The subsoil was idealized as a one-dimensional compression model. The effectiveness of the proposed solution was verified by comparisons with results from the collected literature. It is shown that geogrid reinforcement improved the performance of embankments with low subsoil stiffness significantly more than that of those with high stiffness subsoil. A high tensile stiffness geogrid was found to be inefficient because its contribution to reducing the subsoil settlement and enhancing the load transfer efficacy was minimal. This paper provides a significant reference for optimizing these embankment design.